Bibliographic record
Abstract
Hansen, D. T. (2021). Reimagining the call to teach: A witness to teachers and teaching. Teachers College Press. David Hansen’s book Reimaging the Call to Teach: A Witness to Teachers and Teaching, in very nuanced, subtle, and complicated ways, inquires into teaching as a calling: it enacts and embodies the educational moments and space in teaching filled with uncertainties and complexities as “being with” (p. 33) others, “linking the past, present and future” (p. xiii). Hansen, as it rarely happens, bears an attuned and amplifying witness in sitting in on the classrooms as well as ongoing conversions to 16 public teachers over 2 years who participated in the “Person Project” (p. 52), which assembles teachers’ testimonials about teaching and what it means to “be a person in the world today” (p. 54) without imposing objectifying measurements and standards. In the book, the messy and rough ground of teaching, full of feelings, hesitations, and confusions, is not analyzed and decoded but rather felt, embodied, and described more comprehensively. As Hansen suggests, “I believe what is put forward here is a better description of the unity of the aesthetic, ethical, moral, and intellectual aspects of teaching—of the gestalt of teaching—of the meaning of teaching” (p. 57).
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.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".