Bibliographic record
Abstract
Academic social networking services (SNSs) such as ResearchGate.com or Academia.Edu have recently experienced a surge in popularity (Ortega, 2016). Existing research into academic SNSs have focused on population parameters and social networking usage patterns. Currently, no research has been conducted on the quality of bibliographic metadata on academic SNSs. Bibliographic metadata functions to support user tasks, including finding, identifying, selecting, and obtaining information resources. “Creator” metadata, which describes resource authorship, helps users find and identify digital works in a repository. Additionally, academic researchers rely on author attribution for their professional promotion and prestige, and they are accustomed to scholarly environments which implement standards that support accurate author attribution. This study therefore examines “creator” metadata for University of Alberta publications posted on Academia.Edu, and compares these with publisher created records of the same titles. Metadata quality is assessed through the measurement of completeness, consistency, and accuracy. The study reveals that Academia.Edu “creator” metadata is significantly incomplete compared to publisher metadata, and the frequency of incomplete records increases in proportion to the size of the author cohort. This incompleteness is evidence of poor metadata quality on Academia.Edu. Academia.Edu “creator” metadata is, however, much more consistent than publisher metadata. Finally, accuracy is found to be an inadequate determiner of metadata quality, as the presence of user generated metadata calls into question the conceptual stability of “authenticity” and “authority,” upon which a measure of accuracy depends. This study of metadata quality therefore reveals the complexity and contradiction that underlies this topic. In terms of completeness, Academia.Edu metadata is poor in quality. In terms of consistency, Academia.Edu metadata excels in quality. Finally, the study recommends further investigation into the definition of authority in relation to user-contributed metadata.
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 | 0.025 | 0.173 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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.
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".