Bibliographic record
Abstract
This essay is an autobiographical examination of who and where I am on plane of racial and ethnic relations. As such, it will be written in a spirit of self exploration and scrutiny, measuring my beliefs and feelings against popular theories of race relations in United States. I will focus on specific historical incidents that I believe influenced my place in racial world in which I live. In first portion of article I will evaluate influence demographics and family have had in my life from childhood through adulthood. I will examine beliefs of my parents and peers and how they might have influenced me growing up in middle-class suburbia. Despite influence adults have on development of personality, beliefs and attitudes are shaped further through adult interaction. Therefore, in second portion of this article I will describe influence military service and post military employment in law enforcement has had on my understanding of race, racism, and discrimination. Assimilation theory will be instrument upon which I intend to measure these observations. I will suggest that theory is alone inadequate in explaining racial tenor in United States or at very least, in world in which I live. Assimilation theory describes the more or less orderly adaptation of a migrating to ways and institutions of an established group (Gallagher, 18). Assimilation according to Milton Gordon follows an almost mechanical flow from cultural through civic (Gallagher, 19). Is it possible that American society is still within a state of transition? Possibly, but despite J. Allen Williams and Suzanne Ortega's observation that assimilation varies considerably from one to another (Gallagher, 20), and that mechanical flow may not always be orderly, fact remains that United States institutions are yet to be dominated, or even populated, by any race other than white. My mother and father are both from Massachusetts. My father, John J. Barrett Jr., was son of John Sr., an immigrant Irishman and mother Violet, from Canada (also of Irish decent). He grew up in Newton, Massachusetts, while my mother, Elizabeth Neal, was raised in Framingham. Elizabeth was daughter of George Neal, a firefighter, himself predominantly Irish and Dorothy a church organist and hair dresser. Both of my parents are post World War II 'Baby Boomers.' My father was a police officer in Wrentham, Massachusetts, where I now work in same profession. Looking at my own family, which is predominantly Irish, one can see clearly public service ties so closely linked to early Irish history in United States. Roger Waldinger points out how Irish penetrated public sector at a time when the public sector provided relatively few jobs (Gallagher, 320). The 'good old boy network' soon took care of Irish (especially in Boston), and persists to this day in many public sector jobs. My mother is a nurse at Metro-West Medical Center in Framingham. They divorced when I was in 5th grade, but fortunately for my brother, two sisters and I, we never moved out of Wrentham. I went to elementary school in Wrentham, where I lived until I joined military in 1988. The Thompson's were black family living in Wrentham at time; at least, they were only black people I knew growing up. Wrentham was a post World War II suburb like those described in documentary film Race: Power of an Illusion. I recall being very small and driving through town with my father and having him point to small houses he called 'salt boxes.' He told me these were homes built hastily for returning WWII soldiers. All houses were similar in appearance and structure. Clearly, Wrentham's whiteness is a result of governmental policies of 1940s and 1950s which unfairly excluded minority populations. My America was an America envisioned by Greatest Generation, and speaking more broadly, America that Founding Fathers envisioned as well. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".