Seeking Middle Ground: Analyzing Running Records From the Top and Bottom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Responding to recent challenges to Clay’s Running Records (2019) and their analysis using a three‐cueing system, the authorI examines this reading assessment from an additive perspective of both bottom‐up and top‐down orientations of reading instruction. Endorsing their inclusion among classroom reading assessments, the author I navigates the tension between the two orientations by examining signposts of both that can be found in Running Records. In the discussion, I include a corresponding framework to assist teachers’ interpretation and instructional planning for strategic actions, including searching for, using, and cross‐checking various sources of information; solving words; monitoring; self‐correcting; and maintaining fluency. When applied formatively, Running Records may be an assistive component in classroom reading assessment, yielding instruction targeting automaticity decoding and deeper comprehension.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it