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Record W15175442 · doi:10.1056/nejmcps032575

Understanding Prejudice, Racism, and Social Conflict

2003· article· en· W15175442 on OpenAlexaboutno aff
Stephen G. Atkins

Bibliographic record

VenueNew Zealand journal of psychology · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsnot available
FundersAgency for Healthcare Research and Quality
KeywordsPrejudice (legal term)WifeImmigrationSociologyRacismWhite (mutation)Media studiesClubGender studiesLawPolitical science

Abstract

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Augoustinos, M. & Reynolds, K., (Editors) 2001, London: Sage. ISBN 0 7619 6208 5. 362 pages. Prejudice Viewed from Down-Under A professor at major university in recently told my wife and I that is not in Auckland. Sadly, for reasons I describe below, I'd say is awash with prejudice. In my own life here, I've routinely experienced racist comments around Auckland's far North Shore. It might be lot different in Auckland's city centre (and elsewhere in New Zealand), but it's probably not reasonable to conclude that prejudice just isn't problem here. For instance, it is extremely rare in New Zealand (in my experience) to find professional university-degreed white folks from Britain, Canada, America, or northern Europe stocking grocery market shelves or cleaning toilets for living. It is, unfortunately, quite commonplace to find post-professional university-degreed non-white folks employed here in this way.... and indefinitely so. Most I've met speak excellent English (and probably have since kindergarten). But their accents betray that they are not Brits or Kiwis or North Americans. One immigrant I know of holds postgraduate degrees from two prestigious British universities. He formerly (and quite recently) served as full professor in high-tech field at prestigious university on the Mediterranean. Awarded abundant points on his New Zealand immigration form for his immense technical education, he's been here for few years now--driving taxicab. Another unfortunately typical example: husband and wife in strife-torn Sri Lanka had applied to the NZ immigration service only to be told that they were close enough to qualifying (with their Bachelors degrees) that they should pursue Masters degrees and then re-apply. In desperation, they forced themselves and their children to sacrifice normal family relations so that both parents could continue full-time employment while also completing an intensive (and expensive) full-time MBA programme in Sri Lanka. These parents had their children take care of themselves for two years under these stressful conditions--just to qualify for New Zealand immigration. Having been here over year now, they still work in menial entry-level employment (delivering morning newspapers house-to-house and working late-night shift pumping petrol into cars)--with personal finances so depleted by the move that additional relocation is highly unlikely. Just from my own casual acquaintances, I could easily provide dozen examples like this--I suspect many Aucklanders could. Obviously, the plural of anecdote is not necessarily data (anonymous aphorism) and the role of in this remains an empirical question--but if Kiwi business interests required this kind of deceptive points-based immigration to flatten local salary pressures, it surely has gone far enough (and doubtless went far enough several years ago). Collectively, we've immigrated tens of thousands of people into New Zealand under this sort of false premise--potentially destroying tens of thousands of careers (given the broad gaps this situation is placing into each such immigrant's CV). Over the past few years, I've heard New Zealand media commentators fretting about brain drain--and in this fretting, I don't recall the mention of under-employed immigrants with professional degrees and experience. The factors and motives that have created this sad situation will not all be connected to prejudice, but it's very likely component--and the outcome is shocking. It is, in my view, national travesty--and certainly suggests that may indeed be a in Auckland and elsewhere. Tragically, is nearly ubiquitous and usually insidious. Many of us probably first deal with it overtly and intentionally as kindergartners or primary schoolers via pejorative non-verbal cues or pathetic humor targeting some demographic group. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.202
GPT teacher head0.439
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2003
Admission routes1
Has abstractyes

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