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
In her work concerning algebraic thinking, Kieran notes that students learning algebra tend to fall into two groups—“algebraic” thinkers who use undoing as a way to solve equations, and “arithmetic” thinkers who use trial-and-error substitution to solve equations. “Algebraic” thinkers rely on inverse operations; for example, this group would solve 5 + a = 12 by saying 12 – 5 = 7, ignoring the variable itself. When these students move on to more complex equations, such as 3a + 3 + 4a = 24, they tend to overgeneralize and get stuck (“24 divided by 4, minus 3, minus, um, no, divided by 3”). They are unable to balance the equation because they have not assigned enough significance to the role of the equal sign within the equation- solving process (Kieran 1988, p. 94). When arithmetic learners speak of their solutions, however, because they are using trial-and-error substitution, Kieran finds that they discuss the balance required between the two sides of the equation. She further states that of these two, “arithmetic” thinkers are using a method that “may provide a more intuitive basis for the more structural solving methods” (1992, p. 401). I was curious to see if an eighth-grade student whose thinking could be characterized as “arithmetic” would indeed find this type of thinking a help or a hindrance to her further development of algebraic concepts.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 it