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Record W2778151157 · doi:10.18438/b8h66r

Effect of Undergraduate Research Output on Faculty Scholarly Research Impact

2017· article· en· W2778151157 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEvidence Based Library and Information Practice · 2017
Typearticle
Languageen
FieldComputer Science
TopicScientific Research and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCitationContext (archaeology)VariablesUndergraduate researchRegression analysisLinear regressionLibrary scienceStatisticsMedical educationComputer sciencePsychologyMathematics educationMathematicsMedicineGeography

Abstract

fetched live from OpenAlex

Abstract
 
 Objective – In the context of the ongoing discourse about the role of Institutional Repositories (IRs), the objective of the study is to investigate if there is any evidence of a relation between undergraduate student activity in an IR and the impact of faculty research. 
 
 Methods – The data used for the study is representative of six academic departments of the College of Science and Mathematics (CSM) at California Polytechnic State University (Cal Poly). Digital Commons@Cal Poly (DC) is the IR supported by the library. Regression analysis was used to investigate the interdependence between faculty research impact (dependent variable) and undergraduate student repository activity (independent variable). For each department, faculty research impact was quantified as a measure of the citation counts for all faculty publications indexed in Web of Science (WoS) between January 2008 and May 2017. Student repository activity was quantified for each department in two ways: (1) total number of student projects deposited in DC since 2008 (Sp) and (2) total number of student project downloads from DC (Sd). The dependent variable was regressed against each of the two elements of student repository activity (Sp and Sd), and the resulting statistics (sample correlation coefficients, coefficients of determination, and linear regression coefficients) were calculated and checked for statistical significance. 
 
 Results – The statistical analysis showed that both components of student repository activity are positively and significantly correlated with the impact of faculty research quantified by a measure of the citation counts. It was also found that faculty repository activity, although positively correlated with faculty research impact, has no significant effect on the correlation between student repository activity and faculty research impact. 
 
 Conclusion – The analysis considers two distinct groups of publications: one group of student publications (senior projects) from six academic departments, which are deposited in an open repository (DC), and one group of publications (not necessarily represented in DC) of faculty affiliated with the same six departments and whose citation impact is believed to be affected by the first group. The statistical correlation between student repository activity and faculty research impact can be seen as an indication that an active, open IR centered on collecting, preserving, and making discoverable student research output has a positive impact on faculty’s research impact. More research that includes additional factors and uses a larger data set is necessary to arrive at a definitive conclusion.

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchBibliometrics
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptMetaresearch
Domain: Evaluation · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.017
metaresearch head score (Gemma)0.041
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.041
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0060.354
Open science0.0030.001
Research integrity0.0000.001
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 head0.111
GPT teacher head0.448
Teacher spread0.337 · 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