Supervision Encounters “That Are Not So Nice”: Experiences of Teachers in Guyana
Bibliographic record
Abstract
Expected positive outcomes of teacher supervision in Guyana are not always achieved. This qualitative study explores forces that may be hindering desirable outcomes of supervision provided by Nursery Field Officers (NFOs) and is intended to be an attempt to improve practice. Through thematic analysis of interviews with 30 teachers, five critical areas of undesirable encounters were discovered. These encounters were framed as fault-finding, controlling and mechanically oriented, unproductive, emotionally unsettling, and disruptive. Caution about the danger of identification of pedagogical weaknesses in the absence of accompanying solutions and recommendations is flagged, and remedial strategies are identified. The findings reframe, reinforce, and complement existing knowledge about educational supervision, serve to chronicle Guyanese teachers’ experiences with the supervision of NFOs, and might be informative to professional development programs that rely on supervision to sustain pedagogical growth. Program providers and other stakeholders might find the teachers’ experiences a reference point to consider critical issues regarding the quality of and approach to supervision.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".