Displaying Mathematical Literacy – Pupils’ Talk about Mathematical Activities
Bibliographic record
Abstract
The aim of the study was to exemplify pupils’ mastering of mathematical literacy. The study is a comparative multiple case study. In pupils’ talk of mathematical activities aspects of mathematical literacy are discerned. A distinction is made between pupils: (1) pupils in mathematical difficulties, (2) pupils with another mother tongue than Swedish or (3) pupils without mathematical difficulties. The study was performed as a comparative multiple case study. The “cases” were constituted by the three groups of pupils, and these were compared. Seventy-two pupils in grade 5 in six different primary schools in Sweden participated: twenty-four pupils in mathematical difficulties (twelve girls and twelve boys), twenty-four pupils with another native language than Swedish (twelve girls and twelve boys) and twenty-four pupils without mathematical difficulties (twelve girls and twelve boys). After each of the performed lessons in which the activities were carried out the pupils were interviewed (groupwise in the above defined groups) about their experience of the activities. In the analysis of the results three ideal types were described, one for each group of pupils. The ideal types were discussed with relation to mathematical literacy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".