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Record W2495676762 · doi:10.1057/9780230292543_11

Impatient on the Net: Exploring the Genres of Internet Use for Health

2010· book-chapter· en· W2495676762 on OpenAlexaboutno aff
Maria Bakardjieva

Bibliographic record

VenuePalgrave Macmillan UK eBooks · 2010
Typebook-chapter
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetHealth informationPoint (geometry)Everyday lifeInternet usersPhenomenonInternet privacyPsychologyAdvertisingSociologyBusinessComputer scienceWorld Wide WebPolitical scienceHealth careEconomic growthEconomicsMathematics

Abstract

fetched live from OpenAlex

The growing appetite of Canadians for health information online has been reported in a series of studies showing that the number of households using the internet for that purpose had grown by 262 per cent between 1998 and 2002 (Earl, 2004; Hirji, 2004; Sanders, 2008). Indeed, according to Statistics Canada (2008), of the estimated 15 million Canadians who used the internet from home in 2005, 58 per cent had, at some point, searched online for health information. Given the prevalence of health as an internet search topic, in this chapter I examine the phenomenon at the level of everyday life, to investigate and classify the diverse situations and motives reflected in the concrete instances in which users make decisions about what kind of health information is important for them and instruct their search engines accordingly. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0030.007
Scholarly communication0.0070.006
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.157
GPT teacher head0.395
Teacher spread0.238 · 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 designQualitative
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

Citations0
Published2010
Admission routes1
Has abstractyes

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