Commentary: Reflections on Literacy, Education and a Twenty-Year Inquiry Process
Bibliographic record
Abstract
In this interview Sandra Hollingsworth describes a unique experience in open-ended inquiry that lasted over 20 years.As a new professor at Berkeley she began with a study of her teaching literacy to preservice teachers from a traditional anthropologic perspective. When the study showed that her students had learned "nothing," she invited an informal group of them to share their experiences as beginning teachers learning to teach reading.The group transformed with time and became recurring occasions for all to reflect and learn about topics like social justice in urban schools, multiple literacies, race and other teaching issues. She describes some of the challenges the group encountered when trying to publish its findings and some of the key things she learned from participating in this inquiry—such as the importance of longitudinal inquiry.Finally,she introduces fellow members of the group and describes their current professional endeavours.
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 | 0.035 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.040 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.042 | 0.061 |
| 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".