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Record W7132891911

Facilitating student re-entry meetings: An analysis of the experience and expertise of educators

2025· dissertation· W7132891911 on OpenAlexaboutno aff
Lauren A McPhee

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldSocial Sciences
TopicEducation Discipline and Inequality
Canadian institutionsnot available
Fundersnot available
KeywordsThematic analysisInterviewFacilitationProcess (computing)Qualitative researchChristian ministry
DOInot available

Abstract

fetched live from OpenAlex

At times, young people behave in a manner within the school environment that initiates a school exclusion protocol (suspensions or expulsions), a practice that has been around for decades. When a student is excluded from school the Ministry of Education in Ontario strongly recommends a re-entry meeting prior to their return to the educational environment, however there is little guidance on how this meeting is to be facilitated. The purpose of this study was to contribute to the research on re-entry meeting facilitation by interviewing those who conduct these meetings in order to better understand the factors that make re-entry meetings effective or ineffective. Secondly, this study draws on participant expertise on re-entry meeting facilitation to discern how theoretical research on human motivation and engagement such as Basic Psychological Needs Theory may provide direction on how this process may be improved for students, caregivers, and schools. Ten participants (seven administrators and three school board personnel) were recruited for a 1-hour semi-structured interview. Using codebook thematic analysis, participant responses were coded and analyzed to reflect common themes, experiences, and perspectives. The results of this study provide information that could contribute to enhancing re-entry meeting facilitation by integrating these insights into practical applications and resources.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.479
Teacher spread0.434 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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