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Record W1490725944 · doi:10.3233/wor-2011-1268

Counting the minutes: Administrative control of work schedules and time management of secondary school teachers in Québec

2011· article· en· W1490725944 on OpenAlexafffundabout
Jessica Riel, Karen Messing

Bibliographic record

VenueWork · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsGDG EnvironnementUniversité du Québec à Montréal
FundersCanadian Institutes of Health Research
KeywordsTask (project management)Work (physics)OvercrowdingControl (management)Class (philosophy)Time managementTask managementMedical educationPsychologyClassroom managementMathematics educationComputer scienceMedicineEngineering

Abstract

fetched live from OpenAlex

UNLABELLED: Québec teachers have been identified as having a high level of stress and having difficulties with work-family balancing (WFB). An analysis of their work activity was done to identify task elements that could be changed. PARTICIPANTS: Work of 15 teachers was observed and 20 other teachers were interviewed. METHODS: Ergonomic analysis, a mixed method that combines qualitative analysis with some quantitative data: 87 hours' observation; 15 interviews. Environmental parameters were recorded in 8 classrooms and in two faculty workrooms. Working postures were recorded. RESULTS: Teachers were subject to numerous demands in an often inadequate environment. A new management practice required teachers to spend 300 min/week outside class but in school, where their work could be monitored. The timed and scheduled tasks could not be done in the rooms provided due to overcrowding, inadequate physical environment, and lack of access to computers and telephones. Time at home decreased but work done at home did not. CONCLUSIONS: The physical environment of teaching impacts teaching activity. Work organization that treats a complex, results-oriented task as if it could be well represented by the number of supervised minutes spent on it can be problematic. WFB should be considered when work is re-organized.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.088
Threshold uncertainty score0.384

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 studies0.0000.001
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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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 designObservational
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

Citations18
Published2011
Admission routes3
Has abstractyes

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