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.”
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.118 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads agree on what is shown here.
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".