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Record W4402109772 · doi:10.55016/ojs/jet.v53i3.72253

Hiring Practices and its Connection to the Conceptualization of ‘a Good Teacher’ in Diverse Classrooms

2021· article· en· W4402109772 on OpenAlexaffabout
Daniela Fontenelle-Tereshchuk

Bibliographic record

VenueJournal of educational thought. · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicGlobal Education and Multiculturalism
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsConceptualizationConnection (principal bundle)PsychologyMathematics educationPedagogySociologyComputer scienceMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract: This article explores the role of hiring practices in the conceptualization of what makes a teacher ‘a good teacher’, especially in diverse classrooms. It is based on a multiple case study that examined the perceptions of the experience of four high school English language arts educators teaching in diverse classrooms in Alberta, Canada. These practitioners suggest that hiring practices are mostly unreliable as principals’ hiring decisions are often strongly influenced by noticeable teachers’ personal traits and their preconceived perceptions of what a ‘good’ teacher is. During the hiring process, such interpretation often relies on the sole perceptions of the experience of school principals, which are usually not supported by effective hiring practices research. Hiring ‘the most suitable’ teacher to meet the needs of increasingly complex classrooms is key to education. As education strives for responding to the needs of an increasingly diverse community of learners, reflecting on hiring practices is important to improve the quality of the education provided to students. Teachers’ perspectives on hiring practices are important as they could potentially contribute and inform professional development initiatives, teacher preparation programs, and educational policies regarding a possible connection between administrative roles and teacher effectiveness.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.725

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.071
GPT teacher head0.412
Teacher spread0.341 · 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 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

Citations2
Published2021
Admission routes2
Has abstractyes

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