MétaCan
Menu
Back to cohort
Record W7095653001

PUB TYPE Information Analyses (070) Reports

2016· article· en· W7095653001 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Comparative Analysis Research
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)Table (database)Work hoursAcademic yearPart-time employmentSchool dropoutWorking hoursData collection
DOInot available

Abstract

fetched live from OpenAlex

Research done during the last 10 years on part-time employment of secondary students was compared with results of a study of part-time employment in Toronto (Ontario, Canada). The literature has indicated that whether part-time work is beneficial or harmful depends on the amount of time students spend at work. In comparison with students with no jobs and students who work long hours, students who work limited hours (up to 15 per week) tended to demonstrate superior academic performance. They tended to spend more time on homework and extracurricular activities and to have lower dropout rates. More than 15 to 20 hours a week was associated with negative academic results. Part time employment among Toronto students was explored through the 1991 Every Secondary Student Survey, the Ontario Provincial assessment of student writing, and a local school survey on the after-school activities of 71 students. Data on Toronto students support the findings of other research. Working was more advantageous to students than not working, provided the hours were moderate (up to. 15 hours a week). Four appendixes present tables that summarize the impact of part-time work on students. (Contains 4 figures and 1 table in the text and 32 references.) (SLD) Reproductions supplied by EDRS are the best that can be made from the original document. ssues related to student part-time work: What did research find in the Toronto situation and other context?

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.898
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0100.002

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 head0.353
GPT teacher head0.588
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same topicQualitative Comparative Analysis ResearchFrench-language works237,207