Facilitating student re-entry meetings: An analysis of the experience and expertise of educators
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".