Bibliographic record
Abstract
Upon entering a school as a pre-service teacher you will encounter and observe teachers in action. Your observations will lead to an awareness of the many prerequisites required to educate students. These prerequisites are often labelled quite simply ‘teacher expertise’. Expertise, though, is not precise enough to communicate effectively what the observer has noted either overtly via observation or intuitively via reflection. The pre-service teacher needs to dialogue with expert teachers; however, education is fast-paced and leaves little time for discussion that is neither deep nor accurate because much of what expert teachers do is tacit, unnamed and complex. It is tempting for young teachers to try to emulate experienced educators because they see someone apparently experiencing none of the problems they seem to encounter. In other words, some veteran teachers inadvertently ‘ … reinforce the myth that “good ” teachers encounter few if any uncertainties in their everyday practice and by mitigating against raising questions about practice of self and/or others, the culture of teaching promotes isolation and the virtue of self-reliance ’ (Hannay, 1998: 19). Education does suffer from the ‘constraints of overload, isolation, and compartalization that are endemic to schools ’ (Earl & Cousins, 1995: 42). Therefore, pre-service teachers need to understand expertise before they enter classrooms, so that they can better identify, label and discuss their observations with mentors and peers. What follows are six constructs (see Figure 1) that provide useful concise descriptions of expertise and the associated traits that permeate each.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".