Sensing Expertise in Pre-Service Teacher Education
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it