Thirty years of the Give-N task: A systematic review, reflections, and recommendations
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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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 | 0.034 | 0.104 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".