Reimagining LRA in the Spirit of a Transcendent Approach to Literacy
Bibliographic record
Abstract
This invited paper highlights the reflections of expert panelists who were spontaneously called upon, graciously accepted, and quickly organized to respond thoughtfully and compellingly to Dr. Arlette Willis' powerful and timely Oscar Causey address at the 2022 Literacy Research Association (LRA) annual conference. In her address, Dr. Willis issued a clarion call for a Transcendent Approach to Literacy (TAL) to a space where “We re-create literacy as an equitable and moral construct” (Willis, 2023, p. 133). This paper comprises Dr. Alfred Tatum's comprehensive introduction, the cogent reflections on TAL by panelists Dr. Josh Coleman, Dr. Marcus Croom, Dr. Matthew Deroo, Dr. Michiko Hikida, Dr. Emily Machado, Dr. Patriann Smith, Dr. Chad Waldron, and Dr. Rahat Zaidi, and Dr. Willis' eloquent epilogue. In her epilogue, she provides not an ending but the genesis of a movement forward for the LRA community to “be brave” and actively and genuinely engage in a TAL that “democratizes literacy, declaring literacy belongs to all.”
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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 | 0.048 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.073 |
| Scholarly communication | 0.019 | 0.023 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.007 | 0.025 |
| Insufficient payload (model declined to judge) | 0.004 | 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".