Bibliographic record
Abstract
About GCECFounded in 1997 as the National Consortium of Entrepreneurship Centers with the mission of serving as the "premier leadership organization addressing the emerging topics of importance to the nation's university-based centers for entrepreneurship," the now-expanded Global Consortium of Entrepreneurship Centers (GCEC) today includes a global membership of around 250 centers, representing both well-established and new centers.The consortium is administered by staff at the Jones School of Business at Rice University in Houston, Texas. Nature of the GCEC ConferenceAn organizational membership in GCEC is a prerequisite for associated individuals to attend this conference, which in 2017 drew around 300 entrepreneurship program coordinators and professors and three entrepreneurship librarians.A small number of attendees hailed from outside North America.There were apparently no graduate students present, since this conference does not have a research track.Around 40 people attended the "New Conference Attendee" preconference orientation.The goal of the conference is to "share information, develop programs and initiatives, and collaborate and assist in advancing, strengthening, and celebrating the contributions and impact of individual centers-as well as the overall role of university-based entrepreneurship centers."GCEC covers entrepreneurship education as well as on-campus incubators and start-up accelerators.GCEC member campuses apply to host the annual conference.While the host school is responsible for all conference expenses, it retains 100% of registration fees as well as any GCEC Global Consortium of Entrepreneurship Centers 2017Halifax, Nova Scotia
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.791 | 0.733 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".