Literacy Clinics During COVID-19: Pivoting and Imagining the Future
Bibliographic record
Abstract
Literacy clinics have a long history of providing supplemental assessment and instruction to students with literacy needs, but they were tested during the COVID-19 pandemic, as many pivoted from a face-to face format to three-way remote learning. This study provides a window into how literacy clinics at this moment of transformation in education embraced, and in some cases were challenged by, technology. A survey was administered in spring 2021 to a sample of 58 literacy clinic directors from the United States, Canada, Brazil, Bolivia, The Netherlands, and Australia. Data analysis included quantitative descriptive and inferential statistics reporting on the use of technological platforms and resources, clinic settings, and the format of clinics, before, during, and anticipated after pandemic. Results suggest that clinicians retained some traditional instruction methods while moving some components to digital spaces. Qualitative analysis included (a) coding, (b) creating categories, and (c) developing profiles of respondents based on their prepandemic and postpandemic instructional delivery format. Survey responses conveying the challenges and opportunities of online instruction are discussed in accordance with technology, pedagogy, and content knowledge. This research captured the precipice of institutional change as literacy clinics responded to the pandemic and then recalibrated their intentions for the future.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".