The Pedagogic Signature of the Teaching Profession
Bibliographic record
Abstract
Lee S. Shulman deplores that the field of education as a profession does not have a pedagogic signature, which he characterizes as a synthesis of cognitive, practical and moral apprenticeship. In this context, the following study has three goals: 1) In the first theoretical part, the basic problems of constructing a pedagogic signature are depicted. 2) In the empirical part, based on a multi-method approach, teachers’ and teacher educators’ beliefs and attitudes about a pedagogic signature are identified and ranked. It is argued that beliefs are of particular importance because they have a filter effect on how teachers deal with scientific knowledge. 3) The third part, the discussion, explains the findings, particularly the fact that moral aspects and aspects referring to a particular attitude play an overwhelming role. The explanation leads to some basic considerations on how to construct a pedagogic signature and on how such a signature can be turned into a viable concept for teacher education.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".