Computational Linguistics, Chicano/Mexican Politics & FAQ Linguistics In An Enterprise
Mentions Cultural events in the news, on peoples minds within the Twitter space.
Updates consist of 20ish most recent or available tweets that were deemed newsworthy for the Chicano community. Lots of discussions about skin tones, reactions against hollywood and other
[xyz-ips snippet=”chicanoculture”]
For cultural news, please see here: Chicano Culture.
In this page, we review possibly objectionable content related to Mexicans. We have stored these tweets in a database. Many people make statements on Twitter ‘with a pinch of salt’. However, therein lies a powerful question: who gets to define what is simply a cheeky reference and what crosses the line as cemented or fomenting detrimental worldviews? A few simple questions will help the reader of the tweets figure out what to potentially report to Twitter:
If the answer to any of the above mentioned is “no”, then this tweet is likely objectionable and worth passing along to Twitter.
The social media representation of communities is important. While Freedom of Speech is important too and we should not seek to prevent statements from being uttered/tweeted, we can check their propagation; a racially biased or offensive view must always be countered by a concerted rebuttal.
[xyz-ips snippet=”offensive”]
In an effort to highlight more content, we developed a few database queries to routinely retrieve uncontroversial tweets. Some of these contain frivolous references or insightful comments. Unfortunately, in many social media platforms, some of the least informed content often gets more elevated in the general public’s conscience. This page is an effort to add visibility to the reactions, concerns and ideas of the less prominent (“unliked”, less indexed) voices on Twitter, which are equally valid.
In this page, you can monitor content that contains the keyword ‘Chicano’ without any explicit content. For that more flagrant content, please visit this link. This relatively neutral content should be easy enough to follow along. I sort this list of tweets programmatically; using the Twitter Search API, I am able to amass a daily sampling of tweets on the concepts most.
At any rate, these pages allow one to observe what topics are on the mind of the more vocal members of the community. Feel free to report to twitter any objectionable content. The tweets are shown in their entirety and the views expressed their do not express my own or those of my employer.
The content is refreshed roughly every 24 hours. You will either get today’s results or the day before.
[xyz-ips snippet=”ChicanoNeutral”]
Saul “Canelo” Alvarez is now contemplating challenging a much bigger man who is the champion at the Cruiserweight weight division in the WBC.
The Canelo legacy keeps rising as the 31 year old Mexican enters his prime and relishes success compounded repeatedly after multiple successful defenses of titles. Most recently, the Mexican has unified the competitive 168lb pound division.
His fanbase is expanding globally, with English speakers placing support behind the ‘face of boxing’ amidst the usual controversies and biases that all combat sports tend to manifest.
The Mexican fanbase too looks on as they toil away at their jobs being the backbone of multiple regional and national economies. Every Canelo fight affirms some of the positive image that exists globally of the Mexican man. At least, that is how most sports watchers interpret the presence of Canelo in media depictions. To talk about this legend in development is to talk about the importance of boxing within the Mexican community. Thus, the moves he makes will define the sport for decades to come.
According to Michael Benson, Canelo is planning to weigh at 180lbs as he faces Ilunga Makabu, an opponent with a significant weight and height advantage, 200lbs and much taller.
Canelo is now set to face his last and potentially most difficult fight for Super Middleweight supremacy: Caleb Plant.
The Super Middleweight unification bout is set to kick off tomorrow at around 6pm PT from Las Vegas, Nevada. It’s at 75 dollars, which is not terrible alongside a decent undercard. Already, Canelo is a four-division world champion but Plant is IBF champion. Whoever wins is the first undisputed super middleweight world champion in boxing history. The stakes can not be higher.
At 168 pounds, Canelo looked bulky and ready to deliver powerful blows. He made weight spot-on, 168lbs is the Super Middleweight limit, even going as far as to still wear a heavy gold pendant at the scale. For his part, Caleb Plant weighed in at 167 pounds:
The current IBF title holder at 168 pounds looked muscular as well, but thinner and trim as he is over 6ft tall – a bit of a liability when fighting a compact, explosive opponent.Our best guess is that Caleb Plant’s 167lb frame is an indication that he will fight at distance – “run” as some detractors say – during the fight:
Regardless, this looks to be an historic night with one man ready to unify all the belts. Reportedly, Al Haymon and Eddy Reynoso have been planning or open to additional fights.
Each fighter has a respectable resume, but the best belongs to the current Pound-for-Pound king, Saul “Canelo” Alvarez. He has most recently defeated 2 previously unbeaten Super Middleweights and defended his title against a formidable challenger in Avni Yildrim.
With respect to Plant, he does have 4 title defenses with his best win being over Jose Uzcategui. Mike Lee was also a respectable opponent, but one would be hard pressed to compare either one with Billy Joe Saunders or Callum Smith – the two Brits defeated by Alvarez.
Canelo must KO Plant because as the PBC fighter, Plant is likely set to get the judges nod. Canelo must realize this and is predicting an 8th round KO. It’s tough to take this type of assertion seriously, with many cautioning other fighters about making such predictions. However, in Canelo’s case, most make an exception.
Personally, my fear is that this fight will be boring, with Plant excessively moving as his track leg physique indicated at the weigh-in. I hope I am wrong, but think I may not be.
The Sandor Martin upset is reminiscent of the great Manny “Pacman” Pacquiao upset against Jeff Horn.
In Fresno, California, Mikey Garcia delivered a slow and methodical performance against Sandor Martin. For his part, Martin delivered on the expected southpaw style, consistent jab and constant lateral movement – this proved more valuable to the California judges who exhibited a preference for elusive activity. Martin scored the upset and is now heralded as Spain’s hero in the boxing world.
There is a silver lining of sorts. In recent boxing memory, this Martin upset is not the first time that a relative unknown pulls off an upset against a fighter backed by a devoted fanbase: Jeff Horn’s win over Pacquiao did little to dull Pacquiao star power or even boxing ability. For different reasons, fans can argue giving Garcia a pass: he has returned (grudgingly) after a 20 month fight layoff.
In Pac’s case, fans reasoned he faced a ‘dirty’ fighter whose style was deemed ungentlemanly and home base of Australia provided an unfair judging hand. In a way, the purists who love ‘pure boxing’ and ‘pure officiating’ can signal to the fight as a fair outcome involving a well known boxing star. Garcia fans simply saw a man closing the distance and another engaged in lateral movement. No one saw an embarrassing loss. It was just a boring fight.
The demand stayed constant despite the loss to Horn. Pac-Man moved on and we saw him fight once more for years going towards a title belt in the same welterweight division. Ultimately, Pacquiao never gave Horn a rematch. Curiously, we observe here that it’s likely Garcia will not go out for a Martin.
It is possible that the loss served a few more ends than just catapulting Sandor Martin onto the radar of boxing fans: validate fair judges. It’s likely that Garcia lost the fight technically, but Martins’ style, evasiveness and jab kept Garcia from connecting more than twice a round on his head.
I doubt there will be a rematch.
Like with Horn, the Mikey Garcia fanbase can turn the page and demand another fight with a more offensive minded opponent. Ultimately, the assertion from Robert Garcia about the fight not being even desired Mikey Garcia echoes the thoughts of many fans: Mikey just did not want this fight. According to Garcia, Mikey only took the fight because of the possibility of Bam Rodriguez receiving a title fight. That title fight did not materialize, with the ‘boxing’ and lateral movement winning the day too. A bad night, but undoubtedly, the fans will be happy with a better fight.
Robert Garcia Brutally Honest About Mikey Garcia Fight vs Martin EsNews Boxing – YouTube
In Linguistics and Philosophy of Language, there are various methods and views on how to best describe and justify semantic similarity. This tutorial will be taken as a chance to lightly touch upon very basic ideas in Linguistics. We will introduce in a very broad sense the original concept of semantic similarity as it pertains to natural language.
Furthermore, we will see how the linguistics view is drastically different from the state of the art Machine Learning techniques. I offer no judgments on why this is so. It’s just an opportunity to compare and contrast passively. Keeping both viewpoints in mind during an analysis is helpful. Ultimately, it maximizes our ability to understand valid Machine Learning output.
There is a compositional view that in its earliest 19th century incarnation is attributable to Gottleb Frege, in which the meaning of terms can be decomposed into simpler components such that the additive process of combining them yields a distinct meaning. Thus, two complex meanings may be similar to one another if they are composed of the same elements.
For example, the meaning of ‘king’ could be construed as an array of features, like the property of being human, royalty and male. Under this reasoning, the same features would carry over to describe ‘queen’, but the decomposition of the word would replace male with female. Thus, in the descriptive and compositional approach mentioned, categorical descriptions are assigned to words whereby decomposing a word reveals binary features for ‘human’, ‘royalty’ and ‘male’. Breaking down concepts represented by words into simpler meanings is what is meant with ‘feature decomposition’ in a semantic and linguistic context.
Alternatively, Machine Learning approaches to semantic similarity involves a contextual approach towards the description of a word. In Machine Learning approaches, there is an assignment of shared indices between words. The word ‘king’ and ‘queen’ will appear in more contexts that are similar to one another than other words. In contrast, the words ‘dog’ or ‘cat’, which implies that they share more in common. Intuitively, we understand that these words have more in common due to their usage in very similar contexts. The similarity is represented as a vector in a graph. Each word can have closely adjacent vectors reflecting their similar or shared contexts.
Where both approaches eventually converge is in the ability for the output of a semantic theory or vector driven description of words matches with language users intuitions. In this tutorial and series of examples, we will observe how the Word2Vec module does fairly well with new, recent concepts that only recently appeared in mass texts. Furthermore, these texts are in Mexican Spanish, which implies that the normalization steps are unique to these pieces of unstructured data.
In this series of python modules, I created a vector model from a Mexican Spanish news corpus. Each module has a purpose: normalization.py cleans text so that it can be interpretable for Word2Vec. Normalization also produces the output lists necessary to pass along to Gensim. Scatterplot.py visualizes the vectors.from the model. This corpus was developed as described below.
This dataset of 4000 documents is verified as being relevant to several topics. For this tutorial, there are three relevant topics: {Mexico, Coronavirus, and Politics}. Querying the model for words in this pragmatic domain is what is most sensible. This content exists in the LaCartita Db and is annotated by hand. The annotators are a group of Mexican graduates from UNAM and IPN universities. A uniform consensus amongst the news taggers was required for its introduction into the set of documents. There were 3 women and 1 man within the group of analysts, with all of them having prior experience gathering data in this domain.
While the 4000 Mexican Spanish news documents analyzed are unavailable on github, a smaller set is provided on that platform for educational purposes. Under all conditions, the data was tokenized and normalized with a set of Spanish centric regular expressions.
Please feel free to reach out to that group in research@lacartita.com for more information on this hand-tagged dataset.
There are three components to this script: normalizing, training a model and visualizing the data points within the model. This is why we have SkLearn and Matplotlib for visualization, gensim for training and custom python for normalization. In general, the pipeline cleans data, organizes it into a list of lists format that works for the Word2Vec module and trains a model. I’ll explain how each of those steps is performed below.
normalize_corpus MethodLet’s start with the normalization step which can be tricky given the fact that the dataset can sometimes present diacritics or characters not expected in English. We developed a regular expression that permits us to search and find all the valid text from the Mexican Spanish dataset.
from gensim.models import Word2Vec
import numpy as np
import re
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
plt.style.use('ggplot')
def normalize_corpus(raw_corpus):
"""
This function reads clean text. There is a read attribute for the text.
Argument: a file path that contains a well formed txt file.
Returns: This returns a 'list of lists' format friendly to Gensim. Depending on the size of the
""
raw_corpus = open(raw_corpus,'r', encoding='utf-8').read().splitlines()
#This is the simple way to remove stop words
formatted_sentences=[]
for sentences in raw_corpus:
a_words = re.findall(r'[A-Za-z\-0-9\w+á\w+\w+é\w+\w+í\w+\w+ó\w+\w+ú\w+]+', sentences.lower())
formatted_sentences.append(a_words)
return formatted_sentences
important_text = normalize_corpus(<<file-path>>)Once we generate a list of formatted sentences, which consists of lists of lists containing strings (a single list is a ‘document’), we can use that total set of lists as input for a model. Building the model is likely the easiest part, but formatting the data and compiling it in a usable manner is the hardest. For instance, the document below is an ordered, normalized and tokenized list of strings from this Mexican Spanish News corpus. Feel free to copy/paste in case you want to review the nature of this document:
['piden', 'estrategia', 'inmediata', 'para', 'capacitar', 'policías', 'recientemente', 'se', 'han', 'registrado', 'al', 'menos', 'tres', 'casos', 'de', 'abuso', 'de', 'la', 'fuerza', 'por', 'parte', 'de', 'elementos', 'policiales', 'en', 'los', 'estados', 'de', 'jalisco', 'y', 'en', 'la', 'ciudad', 'de', 'méxico', 'el', 'economista', 'organizaciones', 'sociales', 'coincidieron', 'en', 'que', 'la', 'relación', 'entre', 'ciudadanos', 'y', 'policías', 'no', 'debe', 'ser', 'de', 'adversarios', 'y', 'las', 'autoridades', 'tanto', 'a', 'nivel', 'federal', 'como', 'local', 'deben', 'plantear', 'una', 'estrategia', 'inmediata', 'y', 'un', 'proyecto', 'a', 'largo', 'plazo', 'para', 'garantizar', 'la', 'profesionalización', 'de', 'los', 'mandos', 'policiacos', 'con', 'apego', 'a', 'los', 'derechos', 'humanos', 'recientemente', 'se', 'han', 'difundido', 'tres', 'casos', 'de', 'abuso', 'policial', 'el', 'primero', 'fue', 'el', 'de', 'giovanni', 'lópez', 'quien', 'fue', 'asesinado', 'en', 'jalisco', 'posteriormente', 'la', 'agresión', 'por', 'parte', 'de', 'policías', 'capitalinos', 'contra', 'una', 'menor', 'de', 'edad', 'durante', 'una', 'manifestación', 'y', 'el', 'tercero', 'fue', 'el', 'asesinato', 'de', 'un', 'hombre', 'en', 'la', 'alcaldía', 'coyoacán', 'en', 'la', 'cdmx', 'a', 'manos', 'de', 'policías', 'entrevistada', 'por', 'el', 'economista', 'la', 'presidenta', 'de', 'causa', 'en', 'común', 'maría', 'elena', 'morera', 'destacó', 'que', 'en', 'ningún', 'caso', 'es', 'admisible', 'que', 'los', 'mandos', 'policiales', 'abusen', 'de', 'las', 'y', 'los', 'ciudadanos', 'y', 'si', 'bien', 'la', 'responsabilidad', 'recae', 'sobre', 'el', 'uniformado', 'que', 'actúa', 'las', 'instituciones', 'deben', 'garantizar', 'la', 'profesionalización', 'de', 'los', 'elementos', 'los', 'policías', 'son', 'un', 'reflejo', 'de', 'la', 'sociedad', 'a', 'la', 'que', 'sirven', 'y', 'ello', 'refleja', 'que', 'hay', 'una', 'sociedad', 'sumamente', 'violenta', 'y', 'ellos', 'también', 'lo', 'son', 'y', 'no', 'lo', 'controlan', 'declaró', 'que', 'más', 'allá', 'de', 'que', 'el', 'gobernador', 'de', 'jalisco', 'enrique', 'alfaro', 'y', 'la', 'jefa', 'de', 'gobierno', 'de', 'la', 'cdmx', 'claudia', 'sheinbaum', 'condenen', 'los', 'hechos', 'y', 'aseguren', 'que', 'no', 'se', 'tolerará', 'el', 'abuso', 'policial', 'deben', 'iniciar', 'una', 'investigación', 'tanto', 'a', 'los', 'uniformados', 'involucrados', 'como', 'a', 'las', 'fiscalías', 'sobre', 'las', 'marchas', 'agregó', 'que', 'si', 'bien', 'las', 'policías', 'no', 'pueden', 'lastimar', 'a', 'las', 'personas', 'que', 'ejercen', 'su', 'derecho', 'a', 'la', 'libre', 'expresión', 'dijo', 'que', 'hay', 'civiles', 'que', 'no', 'se', 'encuentran', 'dentro', 'de', 'los', 'movimientos', 'y', 'son', 'agredidos', 'es', 'importante', 'decir', 'quién', 'está', 'tras', 'estas', 'manifestaciones', 'violentas', 'en', 'esta', 'semana', 'vimos', 'que', 'no', 'era', 'un', 'grupo', 'de', 'mujeres', 'luchando', 'por', 'sus', 'derechos', 'sino', 'que', 'fueron', 'grupos', 'violentos', 'enviados', 'a', 'generar', 'estos', 'actos', 'entonces', 'es', 'necesario', 'definir', 'qué', 'grupos', 'políticos', 'están', 'detrás', 'de', 'esto', 'puntualizó', 'el', 'coordinador', 'del', 'programa', 'de', 'seguridad', 'de', 'méxico', 'evalúa', 'david', 'ramírez', 'de', 'garay', 'dijo', 'que', 'las', 'autoridades', 'deben', 'de', 'ocuparse', 'en', 'plantear', 'una', 'estrategia', 'a', 'largo', 'plazo', 'para', 'que', 'las', 'instituciones', 'de', 'seguridad', 'tengan', 'la', 'estructura', 'suficiente', 'para', 'llevar', 'a', 'cabo', 'sus', 'labores', 'y', 'sobre', 'todo', 'tengan', 'como', 'objetivo', 'atender', 'a', 'la', 'ciudadanía', 'para', 'generar', 'confianza', 'entre', 'ellos', 'desde', 'hace', 'muchos', 'años', 'no', 'vemos', 'que', 'la', 'sociedad', 'o', 'los', 'gobiernos', 'federales', 'y', 'locales', 'tomen', 'en', 'serio', 'el', 'tema', 'de', 'las', 'policías', 'y', 'la', 'relación', 'que', 'tienen', 'con', 'la', 'comunidad', 'lo', 'que', 'estamos', 'viviendo', 'es', 'el', 'gran', 'rezago', 'que', 'hemos', 'dejado', 'que', 'se', 'acumule', 'en', 'las', 'instituciones', 'de', 'seguridad', 'indicó', 'el', 'especialista', 'apuntó', 'que', 'además', 'de', 'la', 'falta', 'de', 'capacitación', 'las', 'instituciones', 'policiales', 'se', 'enfrentan', 'a', 'la', 'carga', 'de', 'trabajo', 'la', 'falta', 'de', 'protección', 'social', 'de', 'algunos', 'uniformados', 'la', 'inexistencia', 'de', 'una', 'carrera', 'policial', 'entre', 'otras', 'deficiencias', 'la', 'jefa', 'de', 'la', 'unidad', 'de', 'derechos', 'humanos', 'de', 'amnistía', 'internacional', 'méxico', 'edith', 'olivares', 'dijo', 'que', 'la', 'relación', 'entre', 'policías', 'y', 'ciudadanía', 'no', 'debe', 'ser', 'de', 'adversarios', 'y', 'enfatizó', 'que', 'es', 'necesario', 'que', 'las', 'personas', 'detenidas', 'sean', 'entregadas', 'a', 'las', 'autoridades', 'correspondientes', 'para', 'continuar', 'con', 'el', 'proceso', 'señaló', 'que', 'este', 'lapso', 'es', 'el', 'de', 'mayor', 'riesgo', 'para', 'las', 'personas', 'que', 'son', 'detenidas', 'al', 'tiempo', 'que', 'insistió', 'en', 'que', 'las', 'personas', 'encargadas', 'de', 'realizar', 'detenciones', 'deben', 'tener', 'geolocalización', 'no', 'observamos', 'que', 'haya', 'una', 'política', 'sostenida', 'de', 'fortalecimiento', 'de', 'los', 'cuerpos', 'policiales', 'para', 'que', 'actúen', 'con', 'apego', 'a', 'los', 'derechos', 'humanos', 'lo', 'otro', 'que', 'observamos', 'es', 'que', 'diferentes', 'cuerpos', 'policiales', 'cuando', 'actúan', 'en', 'conjunto', 'no', 'necesariamente', 'lo', 'hacen', 'de', 'manera', 'coordinada']We build the model with just a few lines of python code once the lists of lists are contained in an object. The next step is to provide these lists as the argument of the Word2Vec in the object important_text. The Word2Vec module has a few relevant commands and arguments, which I will not review in depth here.
from gensim.models import Word2Vec
important_text = normalize_corpus(<<file-path>>)
mexican_model = Word2Vec(important_text, vector_size=100, window=5, min_count=5, workers=10)
mexican_model.save("NewMod1el.w2v")scatterplot Method: Visualizing DataThe scatter plot method for vectors allows for quick visualization of similar terms. The scatterplot function uses as an argument a model that contains all the vector representations of the Spanish MX content.
def scatter_vector(modelo, palabra, size, topn):
""" This scatter plot for vectors allows for quick visualization of similar terms.
Argument: a model containing vector representations of the Spanish MX content. word
is the content you're looking for in the corpus.
Return: close words
"""
arr = np.empty((0,size), dtype='f')
word_labels = [palabra]
palabras_cercanas = modelo.wv.similar_by_word(palabra, topn=topn)
arr = np.append(arr, np.array([modelo.wv[palabra]]), axis=0)
for wrd_score in palabras_cercanas:
wrd_vector = modelo.wv[wrd_score[0]]
word_labels.append(wrd_score[0])
arr = np.append(arr, np.array([wrd_vector]), axis=0)
tsne = TSNE(n_components=2, random_state=0)
np.set_printoptions(suppress=True)
Y = tsne.fit_transform(arr)
x_coords = Y[:, 0]
y_coords = Y[:, 1]
plt.scatter(x_coords, y_coords)
for label, x, y in zip(word_labels, x_coords, y_coords):
plt.annotate(label, xy=(x, y), xytext=(0, 0), textcoords='offset points')
plt.xlim(x_coords.min()+0.00005, x_coords.max()+0.00005)
plt.ylim(y_coords.min()+0.00005, y_coords.max()+0.00005)
plt.show()
return palabras_cercanas
scatter_vector(modelo, 'coronavirus', 100, 21)The coronavirus corpus contained here is Mexico centric in it’s discussions. Generally, it was sourced from a combination of mainstream news sources, like La Jornada, and smaller digital only press, like SemMexico.
We used ‘Word2Vec’ to develop vector graph representations of words. This will allow us to rank the level of similarity between words with a number between 0 and 1. Word2Vec is a python module for indexing the shared context of words and then representing each as a vector/graph. Each vector is supposed to stand-in as a representation of meanning proximity based on word usage. We used Word2Vec to develop a semantic similarity representation for Coronavirus terminology within news coverage.
In this set of about 1200 documents, we created a vector model for key terms in the document; the printed results below show how related the other words are related to our target word ‘coronavirus‘. The most similar term was ‘covid-19’, virus and a shortening ‘covid’. The validity of these results were obvious enough and indicate that our document set contains enough content to represent our intuitions of this topic.
[('covid-19', 0.8591713309288025),
('virus', 0.8252751231193542),
('covid', 0.7919320464134216),
('sars-cov-2', 0.7188869118690491),
('covid19', 0.6791930794715881),
('influenza', 0.6357837319374084),
('dengue', 0.6119976043701172),
('enfermedad', 0.5872418880462646),
('pico', 0.5461580753326416),
('anticuerpos', 0.5339271426200867),
('ébola', 0.5207288861274719),
('repunte', 0.520190417766571),
('pandémica', 0.5115000605583191),
('infección', 0.5103719234466553),
('fumigación', 0.5102646946907043),
('alza', 0.4952083230018616),
('detectada', 0.4907490015029907),
('sars', 0.48677393794059753),
('curva', 0.48023557662963867),
('descenso', 0.4770597517490387),
('confinamiento', 0.4769912660121918)]
One of the measures for the merit of a large machine learning model is if the output aligns with the intuition of a human judgement. This implies that we should ask ourselves if the topmost ranked ‘similar’ words presented by this word2vec model matches up with our psychological opinion of ‘coronavirus’. Overwhelmingly, the answer is ‘yes’, since Covid and Covid19 nearly always mean the same thing, without a hyphen or if referenced as just ‘virus’ in some texts’.
Better normalization leads to better vectors.
This is verifiable in a scatterplot comparing the distinct text normalization that one intuits is best upon analyzing initial training data.
For example, many place names are effectively compound words or complex strings which can lead to misleading segmentation. This adds noise, effectively misaligning other words in the word vector model. Therefore, finding a quick way to ensure place names are represented accurately helps other unrelated terms surface away from their vector representation. Consider this below scatterplot where the names ‘baja california sur’ and ‘baja california’ are not properly tokenized:
Replacing the spaces between ‘Baja California Sur’, ‘Baja California’, and ‘Sur de California’, allows for other place names that pattern similarly to shine through in the scatterplot. This reflects more accurate word vector representations.
From May 2nd to May 5th of 2020, I gathered data with an online survey administered with the help of Mexican based Data Analysts who helped me recruit participants and review the data. My goal was to understand how COVID-19 affected my community’s economic status and employment prospects.
The descriptions here apply to 77 confirmed Mexicans, with 48 Mexican individuals verified through known connections and ‘friends-of-friends’ in social media. The overall sample was larger than 160 individuals surveyed online. All were Spanish speaking, thus, classified as ‘Latino’ at a minimum if they did not opt into the ‘Mexican’ category.
We wanted to know the average salary of Mexicans based in the United States; we wanted to know how Mexicans fared during the peak periods of uncertainty in the pandemic. We consider the peak period of uncertainty to be early March 2020 to June 2020 during the pandemic when no vaccine was available and the most stringent lockdowns took place.
Ultimately, we suspect that the salary prospects of Mexicans during the COVID-19 lockdown were mitigated by their participation in the construction and education sectors of the economy.
We wanted to maximize privacy, but understand the moment faced by the Mexican community. During this time period, remittances to Mexico broke records. Unfortunately, many erroneously attributed Mexican resilience to a meager and occasional stimulus check. Those Mexicans with deeper ties to Mexico tended to not have access to the stimulus check; their legal status as economic migrants would not support the idea.
There was also an additional need to hone in on Mexicans specifically, as most research tends to homogenize distinct communities under ‘Latino’. In this research, the outcomes for the entire population of Spanish speaking individuals with ties outside of the US are worse than what is presented in this report.
Ultimately, the data found within this survey is in harmony with the fact that Mexicans somehow found more work or income to send as remittances to Mexico during the multiple lockdowns.
To confirm we were indeed surveying self-identified Mexican individuals, we administered the survey in Spanish; we also exclusively distributed this survey within closed networks of the Mexican community. However, we also went through a paid-tier of Survey Monkey to examine whether those results remained consistent or affected the overall trajectory of the average salary range for Mexicans in the US. The act of including Survey Monkey participants raised the minimum average salary range too.
Due to mandatory social distancing, the usage of online survey methods was the only acceptable way to take a survey data-based snapshot of our community.
The survey results indicated that about half of Mexicans were not impacted in their employment or hours worked during the Coronavirus due to their links to education, clerical, health and construction sectors.
From the ‘most-trusted’ group of 48 survey participants who were verified through social media and prior in-person interactions, we determined that the average salary range was from US 47,347 dollars to 67,243 dollars for Mexicans. Including, the Survey Monkey group raises the lower range to 48,000 and upper range stays flat.
If we remove individuals who are underemployed due to their status as students, restaurant workers (many of whom received some moderate amount of unemployment or switched sectors), then the minimum average salary is raised to over 56 thousand dollars.
Thus, we could surmise that the average Mexican is making over 56 thousand dollars if active and fully employed in the labor market.
To be more exact, here is the average salary range for the fully employed Mexicans surveyed:
| 57812.5 | 67243.94 |
Survey participants were asked which salary range their annual income fell under. Nearly half of Mexicans surveyed selected more than 50,000 annually.
When averaged out, the bottom range for an Average Mexican salary – for the participants surveyed in both SM and via closed networks – is 48571 US dollars annually to 67243 dollars annually:
| 48571.43 | 67243.94 |
If we included Survey Monkey based participants, we see a bump in Mexican minimum salaries jump to 51000 dollars annually.
The Coronavirus impacted about half of the Mexicans – mostly women – surveyed when it came to the number of hours worked. Those who were impacted tended to have major losses in hours worked. The rest lost between 1 to less than 20 hours of work.
Our numbers vary due to the exclusion or inclusion of Survey Monkey based respondents. If we went outside of our vetted participants, we received noisy data from individuals who can not be confirmed as Mexican or whom may not be operating in good faith. Nonetheless, income averages creeped higher and unemployment rates lowered when including these unvetted Mexicans.
Most Decision Makers Are White
EEOD DATA Indicates 99 out of 123 Execs Are White.
Graphic of @Apple executives based on most recent EEOD data
Recently, the National Labor Relations Board sued Apple. According to the suit, Apple has fired employees in a retaliatory fashion. The employee in question had alleged Sexual Harassment by a male colleague. Unfortunately, Apple management notified the accused party, shifted the victim towards a different line of work within the company and eventually fired the employee after having placed her on administrative leave.
With this recent news in mind, we wondered about the basic demographics of Apple executives. Generally, these are white people in control of major day to day decisions regarding personnel.
A lot of dollars are going to that elite cadre, perhaps, changing and influencing generations of professionals within the IT industry as well as creating generational wealth for those within their demographic:
An environment with plenty of checks and measures may be necessary so that a single demographic is not overly represented. Perhaps, HR could recruit outside of Silicone Valley or the company could establish a presence in urban centers with a high concentration of demographics lacking in its ranks. Accidents happen, perhaps, engineers just recruit or reference who they know and this leads to a legacy of ‘friends of friends’ joining the company.
For now, the majority of Apple Execs are white: 80 percent. The rest are Asian, with 2 Hispanic individuals and 1 lone Black person.
CBS Oakland reporter, Betty Yu, posted on her Instagram account disturbing footage of a group of males who robbed and beat an elderly Asian woman as she had exited a bank in broad daylight.
The recent spat of attacks comes on the heels of disparate racialized events involving Asian Americans. In the past few weeks, Asians have been subject to verbal harassment due to Coronavirus disinformation.
In a parallel trend, there has also been a crime spree and increase of physical assaults on mostly older Asian people walking alone. The underlying animosity is evident, but the targets have routinely been victimized due to their perceived vulnerability. This recent Oakland robbery may be part of this trend.
Many commentators hold that independent of the race of the aggressors, bad actors are reacting to propaganda related to China fearmongering and disinformation surrounding the pandemic.
