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Record W1968500075 · doi:10.1177/1741143204041880

Part-Time Politics

2004· article· en· W1968500075 on OpenAlexaffabout
Beth Young, Mary Alice Brooks

Bibliographic record

VenueEducational Management Administration & Leadership · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsRestructuringElement (criminal law)Power (physics)SociologyPoliticsPublic relationsPedagogyPolitical scienceLaw

Abstract

fetched live from OpenAlex

During a multi-district study on part-time teaching employment, part-time teachers, school administrators, and teachers’ association officials spoke about a range of micropolitical activities related to part-time teaching arrangements as a component of flexible workforces and as an element of flexible workplaces. The study was conducted in one Canadian province during a period of rapidly imposed changes, including funding cuts, typical of the globalized school restructuring movement. We describe administrators’ strategic efforts to mobilize their biases related to part-time teaching employment by organizing their micropolitical resources either to support that preference or to minimize such arrangements. We discuss the strategies that part-time teachers used in order to survive as flexible workers, and sometimes to flourish in rather fragile versions of flexible workplaces. We conclude by noting micropolitical themes that have application beyond the little-studied subculture of part-time teaching. These themes offer insights about power, perceptions, values, and gender that apply more widely to the micropolitics of school organizations.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.008
Scholarly communication0.0050.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.001

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 head0.224
GPT teacher head0.396
Teacher spread0.172 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations5
Published2004
Admission routes2
Has abstractyes

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