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Record W2062659188 · doi:10.1016/j.gheart.2014.03.1251

O035 Current Victorian antenatal detection rates of congenital heart disease in infancy

2014· article· en· W2062659188 on OpenAlex
Natalie Soszyn, Darren Hutchinson, Ricardo Palma‐Dias, Michael Cheung, Bryn Jones

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

VenueGlobal Heart · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsMonopolyCompetition (biology)PreferencePower (physics)Contrast (vision)MedicineEconomicsMicroeconomicsComputer science

Abstract

fetched live from OpenAlex

We study the effect of different levels of information on two-sided platform profits—under monopoly and competition. One side (developers) is always informed about all prices and therefore forms responsive expectations. In contrast, we allow the other side (users) to be uninformed about prices charged to developers and to hold passive expectations. We show that platforms with more market power (monopoly) prefer facing more informed users. In contrast, platforms with less market power (i.e., facing more intense competition) have the opposite preference: they derive higher profits when users are less informed. The main reason is that price information leads user expectations to be more responsive and therefore amplifies the effect of price reductions. Platforms with more market power benefit because higher responsiveness leads to demand increases, which they are able to capture fully. Competing platforms are affected negatively because more information intensifies price competition.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.449

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 communication0.0000.002
Open science0.0000.000
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 head0.012
GPT teacher head0.235
Teacher spread0.224 · 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