Thirty years of the Give-N task: A systematic review, reflections, and recommendations
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
The Give-N (give-a-number) task has become a popular assessment of children’s number words and counting knowledge since Wynn’s (1990, 1992) seminal work over 30 years ago. Using the Give-N task, numerous studies have shown that children learn the first few number words slowly, before they understand how counting represents number. This learning trajectory and children's associated behaviors on the Give-N task are represented by “knower-levels” and form the basis for a large body of research assessing children’s number learning. Recent research has started to critically analyze the theoretical conceptualisation and reliability of knower-levels. We added to this work by conducting a systematic review of studies using the Give-N task. This review provides an overview of methodological practices and variations in the task’s administration and scoring of knower-levels which have theoretical and methodological implications. We argue that advancing methodology and theory for research in children’s number learning requires (1) consideration of Give-N task administration and scoring in study design and reporting and (2) reflection on the assumptions and limitations of classifying children’s performance on the Give-N task in the knower-level framework.
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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 | 0.005 |
| 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 | 0.000 |
| 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