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Record W3020950453 · doi:10.14783/maruoneri.727406

EMPOWERING POSTGRADUATE STUDENTS THROUGH ONLINE CAREER MENTORING

2020· article· en· W3020950453 on OpenAlexaboutno aff
Ellis Rubinstein

Bibliographic record

VenueÖneri Dergisi · 2020
Typearticle
Languageen
FieldMedicine
TopicAdvances in Oncology and Radiotherapy
Canadian institutionsnot available
Fundersnot available
KeywordsPacePublic relationsChinaPolitical scienceDisciplineGeography

Abstract

fetched live from OpenAlex

Many of today's postgrads and postdocs—be they in the United States or in Europe—are interested in exploring the widest possible range of scientific career options, everything from positions in academia and industry to careers 'off-the- bench but too often, they don 't know where to start, and, at the same time, lack knowledgeable mentors. Moreover, the pace of change within the scientific profession is accelerating. Suddenly, the benefits of multi-disciplinary career paths, mobility, and global collaborations outweigh their costs in complexity. Many university systems are coming to recognize this but are struggling to reform their traditional systems. And the pace of these changes threatens to outstrip the ability of administrators and faculty to keep current with new needs. In this environment, Science's Next Wave provides a broad array of novel Services while achieving economies of scale for subscribing institutions. Each week, Next Wave publishes online original articles providing career advice to young scientists and those who would mentor them. Över its 6 years of existence, the Next Wave website has developed an unmatched, living archive of information on career planning, job hunting, grant-writing, education reform, and much more. And it has become increasingly global, going beyond its starting point in the U.S. to develop collaborations with pan- European organizations and national bodies. For example, it is partnering with China’s national organizations and has established homepages in the UK, Canada, and Germany. Next to join: the Netherlands and Singapore.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.548

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 communication0.0000.000
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 head0.057
GPT teacher head0.441
Teacher spread0.384 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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