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Investigation of British Columbia Entrepreneurs' Secondary Market Research Habits and Information Needs

2020· article· en· W3112345600 on OpenAlexaffvenueabout
Aleha McCauley, Irena Trebic, Kim Buschert, Nick Rochlin, Laura Thorne

Bibliographic record

VenueTicker The Academic Business Librarianship Review · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsBusinessMarketingAgricultural economicsEconomics

Abstract

fetched live from OpenAlex

Entrepreneurial research is of increasing significance to North American universities. University libraries have developed or enhanced services and supports for entrepreneurial researchers, to differing degrees. Currently, University of British Columbia (UBC) Library offers secondary research support to both campus and community entrepreneurs. In fall 2017, UBC librarians conducted a study in order to better understand the research habits and information related needs of entrepreneurial researchers, as they relate to the development of business ventures at UBC and in British Columbia. The findings present opportunities for UBC librarians to create innovative services to attract and engage with entrepreneurs more completely. In order to meet the needs of BC entrepreneurs, possible considerations include targeting early-stage entrepreneurs (especially for library workshops), exploring access options and collections for entrepreneurs, developing and promoting online services, and having a service model with a single service entry point for entrepreneurs. Thorough marketing of entrepreneurial services is needed to ensure that the library is seen as a valuable resource. In addition to developing services and collections, forming partnerships with campus groups, community agencies and business groups is another way to increase awareness of library support for entrepreneurs.

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 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)0.003
metaresearch head score (Gemma)0.002
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: none
Teacher disagreement score0.432
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.106
GPT teacher head0.331
Teacher spread0.225 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations6
Published2020
Admission routes3
Has abstractyes

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