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Record W198319396 · doi:10.15388/infedu.2006.02

Structure, Scoring and Purpose of Computing Competitions

2006· article· en· W198319396 on OpenAlexaff
Gordon V. Cormack, Ian Munro, Troy Vasiga, Graeme Kemkes

Bibliographic record

VenueInformatics in Education · 2006
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCONTESTOlympiadCompetitor analysisCompetition (biology)Computer scienceTest (biology)Mathematics educationInformaticsInclusion (mineral)PsychologyMarketingPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

We identify aspects of computing competition formats as they relate to the purpose of these competitions, both stated and tacit. We consider the major international competitions - the International Olympiad for Informatics, the ACM International Collegiate Programming Contest, and top coder - and related contests whose format merits consideration. We consider the operational impact and possible outcomes of incorporating several of these aspects into scholastic competitions. We advocate, in particular, that contests be designed so as to provide a rewarding experience and opportunity for achievement for all competitors; not just the winners. Specific contest elements that should be considered are: (1) real-time scoring and feedback, (2) rewards for testing and test case creation, (3) tasks with graduated difficulty, (4) collaborative tasks, (5) practice contests and entry-level contests for novices, and (6) inclusion of spectators.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.015
metaresearch head score (Gemma)0.067
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.067
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0040.002
Scholarly communication0.0110.003
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0150.004

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.

Opus teacher head0.005
GPT teacher head0.247
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2006
Admission routes1
Has abstractyes

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