Bibliographic record
Abstract
I would like to ask you to remember only this one thing," said Badger."The stories people tell have a way of taking care of them.If stories come to you, care for them.And learn to give them away where they are needed.Sometimes a person needs a story more than food to stay alive; that is why we put these stories in each other's memory.This is how people care for themselves."(Lopez, 1990, p. 48) 2 Recordar: (Spanish).To remember; from the Latin re-cordis, to pass back through the heart-Eduardo Galeano 3 As a Canadian curriculum scholar I often use my own life as a site for inquiry, and as a university instructor I encourage both pre-service and veteran teachers to do the same.Such autobiographical inquiry is neither the dominant form of research in Canadian curriculum studies, nor should it be.Yet I believe this research matters for curriculum theory, for the field, and for the researcher, and as such deserves a place in the academy.While it is true that what defines a text as autobiographical is (a) the presence and significance of the narrative I (Kadar, 1993), and (b) the disclosure of this self through and in the text (Lopate, 1994), this does not mean that the I is necessarily the subject of the text.I find it difficult to defend, as research, some autobiographical narratives, particularly those where the researcher makes himself, most often his psychological or
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 | 0.344 | 0.448 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.030 | 0.135 |
| Scholarly communication | 0.055 | 0.083 |
| Open science | 0.007 | 0.031 |
| Research integrity | 0.051 | 0.050 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".