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Record W4234881039 · doi:10.4324/9781410613196-16

Teacher Research, Professional Growth, and School Reform

2006· book-chapter· en· W4234881039 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPedagogyMathematics educationProfessional developmentPsychologyPolitical scienceSociologyEngineering ethicsEngineering

Abstract

fetched live from OpenAlex

Canadian research on the experiences and aspirations of children of immigrant parents consistently shows that they tend to have high educational and occupational aspirations (Anisef, Axelrod, Baichman-Anisef, James, & Turrittin, 2000 ; Dei, Muzzuca, McIsaac, & Zine, 1997 ; James, 1997 ; Lam, 1994 ; see also Schecter & Bayley, 2002 ). For many of these students, particularly those from working-class backgrounds, these educational aspirations reflect an optimism that seems to ignore the limitations and barriers related to their social, educational, and financial situation. In other words, in spite of their socioeconomic situations, many of these students expect to attend university or aspire to careers that require postsecondary education even though they are in educational streams or levels that do not necessarily qualify them to enter postsecondary institutions. 1 Also, the schools some of these students attend typically send very few students to universities or colleges. But despite these limitations, some of these working-class immigrant students actually manage to overcome the social and educational barriers or hurdles and realize their aspiration of attending university.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.998
Threshold uncertainty score0.165

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.008
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.235
GPT teacher head0.449
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
DomainMethods
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

Citations1
Published2006
Admission routes1
Has abstractyes

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