Free to Learn: Why Unleashing the Instinct to Play Will Make Our Children Happier, Self-Reliant, and Better Students for Life
Bibliographic record
Abstract
Free to Learn: Why Unleashing the Instinct to Play Will Make Our Children Happier, Self-Reliant, and Better Students for Life Peter Gray New York: Basic Books, 2013. Notes, bibliography, index. 274 pp. $27.99 cloth. ISBN: 9780465025992Robert Paul Smith, in a 1957 memoir of his youth, Where Did You Go? Out. What Did You Do? Nothing, mused that one time he discovered while playing that if he could apply his finger to a spinning phonograph record, he could manipulate its speed to produce different kinds of sound. This, he recalled, believe, is science, and I found it out for myself. Peter Gray would rejoice because, to him, Smith's playful curiosity was what education should be. In Free to Learn, a passionate paean to the kind of free and free learning exemplified by Smith's example, Peter Gray, an evolutionary psychologist at Boston College, makes a largely compelling case that children learn best when unencumbered by adult-imposed activities and institutions. Naturally curious, children need to be free to learn from each other and from their own self-structured experiences. If encouraged to do so, Gray argues, children will be happier, more self-reliant, and better students for life. Gray uses two models to elucidate his argument: hunter-gatherer societies of the distant past and the Sudbury Valley School in present-day Framingham, MA. Children in hunter-gatherer cultures, he claims, were (and are, in those tribes still existing today) unfettered by adult rules. They could roam freely and learn by experiment and by observing older children and adults. They mingled their play-filled with vital knowledge and smoothly became contributing members of their societies as they matured, learning in the process to control their impulses and emotions. Gray does not mention that these peoples never learned how to prevent famine and disease, but his appreciation for the self-structured lives of hunter-gatherer children has merit. Eventually, according to Gray, the transformations from hunter-gatherer to settled, agricultural societies turned children into workers for the family, reducing their freedom and wedging them more securely under their parents' thumbs. The learning process thereby became more rigid. Capitalism and industrialization exacerbated the situation, merging education with obedience. The larger society took control of schooling and made it compulsory. Learning became work and play became the enemy of learning.Gray's cursory history lesson provides the entry to his celebration of the Sudbury Valley School. Located in a bucolic setting, this school educates children ages six to eighteen, has no teachers (only adult staff members), no prepared curriculum, no grades, no admission requirements (only an interview), and relatively low tuition. Children learn simply by following their own interests, mingling in interage groups, and participating in a totally democratic environment. …
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".