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Record W2016069715 · doi:10.1145/2070336.2070352

How to make ada go 'viral'

2011· article· en· W2016069715 on OpenAlex
Jean-Pierre Rosen, Brad Moore, Tucker Taft

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueACM SIGAda Ada Letters · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicLanguage and cultural evolution
Canadian institutionsGeneral Dynamics (Canada)
Fundersnot available
KeywordsPopularityMainstreamPoint (geometry)Computer scienceViral marketingWorld Wide WebAdvertisingInternet privacyBusinessPsychologyPolitical scienceSocial psychologySocial media

Abstract

fetched live from OpenAlex

Every Ada user wonders why Ada doesn't not enjoy the popularity it deserves. Experience shows that once people have been properly trained to it, they generally don't want to return to other languages -- but getting the word to the public-at-large -- marketing - has always been a weak point of Ada. Can we change this situation? Recent indicators, like the ?? language popularity rating, shows some positive indication, with Ada's popularity fast rising. This panel brings together experts with various backgrounds to share their views on how to make Ada more mainstream.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.279
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.253
Teacher spread0.221 · 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