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Record W2158914747 · doi:10.5817/proin2010-1-5

Co dělají profesionálové z oblasti konkurenčního zpravodajství? Pilotní studie

2010· article· cs· W2158914747 on OpenAlexaboutno aff
Tao Jin, France Bouthillier

Bibliographic record

VenueProInflow Časopis pro informační vědy · 2010
Typearticle
Languagecs
FieldBusiness, Management and Accounting
TopicCompetitive and Knowledge Intelligence
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesLibrary scienceTheologyArtPhilosophyComputer science

Abstract

fetched live from OpenAlex

Řada lidí si nedovede představit, čím se zabývají profesionálové z oblasti konkurenčního zpravodajství (competitive intelligence, CI). K pochopení praktické náplně jejich práce byl navržen systémový průzkum. V této studii je popsána pilotní fáze projektu.Překlad Barbora Sedláčková Přeloženo z anglického originálu:JIN, T.; BOUTHILLIER, F. What Do Competitive Intelligence Professionals Do? : A Pilot Study. InInformation Sharing in a Fragmented World: 35th Annual Conference of the Canadian Association for Information Science [online]. Montreal : McGill University, 10. - 12. 5. 2007 [cit. 2010-06-20]. Dostupné z WWW: . Děkujeme autorům za svolení k překladu a publikování článku.

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)0.012
metaresearch head score (Gemma)0.037
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.037
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0020.004
Scholarly communication0.0120.009
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.002

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 head0.025
GPT teacher head0.280
Teacher spread0.255 · 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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