MétaCan
Menu
Back to cohort
Record W3127374156

Principals’ Work in Ontario, Canada: Changing Demographics, Advancements in Information Communication Technology and Health and Wellbeing

2016· article· en· W3127374156 on OpenAlexaboutno aff
Katina Pollock

Bibliographic record

VenueScholarship@Western (Western University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsDemographicsWork (physics)Public relationsInformation and Communications TechnologyGeographySociologyPolitical scienceEngineeringComputer scienceWorld Wide WebDemography
DOInot available

Abstract

fetched live from OpenAlex

Contemporary agendas of high-stakes accountability initiatives, national and international competitiveness drives, and standardized curriculum policies have significantly influenced the work of principals. This article explores how these changes influence the work of Ontario principals in English-speaking public schools. We know the changing nature of principals’ work, as compared to the work of teachers, has not been as well represented in the literature and research. Over the last two decades in Ontario, school principals have had to deal with sweeping reform measures that have re-engineered and reconfgured the educational terrain of school administration and leadership. This article takes a broad approach to understanding what contemporary principals do. Among other things, it acknowledges the wide-ranging, diverse and complex nature of what principals do. Most importantly, it adopts the concept of ‘work’ to explore principals’ worlds. While the study in this article utilized a mixed-methods approach using interviews and school site observations, findings reported in this article come from the principal interviews only. This article focuses specifically on changing student demographics, information and communication technology, and health and wellbeing

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 score0.927
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.055
GPT teacher head0.315
Teacher spread0.260 · 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
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueScholarship@Western (Western University)Same topicOnline and Blended LearningFrench-language works237,207