Bibliographic record
Abstract
ATRICK COLLISON, who in his own words is a "hacker, pilot, student at MIT, cofounder of Auctomatic," and "lover of waffl es," certainly can be seen as prototypical of certain fi rst-generation developers of iPhone apps-the whiz kids. 1 Self-taught, he started to program software at an early age.When Patrick was seventeen, he founded his own company, Auctomatic, with his younger brother John and sold it two years later for an exorbitant sum to the Canadian company Live Current Media.During the winter of 2007 he programmed the iPhone app Encyclopedia, an offl ine version of Wikipedia that allows almost all of Wikipedia's online functions, including the use of links between different entries and in 2010 was offered in eightythree languages, including Chinese, Hindi, and Vietnamese.In a broad sense, Patrick Collison is an example of a "digital native."But he is more than that, given that "digital natives" need not, by defi nition, be creative in developing tools; they need only be highly literate in exploiting predefi ned structures.As I will argue, following the writings of the media philosopher Vilém Flusser, it would be a gross misunderstanding to believe that a technology brings forth mental structures or abilities.More often than not, as my investigations into the history of technological change have revealed, thought models develop in a wider cultural context before they result in new technologies, which in turn infl uence patterns of behavior and thus
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".