Surveying the Surveyors: An Analysis of the Survey Response Rates of Librarians
Bibliographic record
Abstract
It is a well-established fact that the most widely employed research method by librarians is surveys. Given this fact, this ongoing study seeks to identify exactly how likely librarians are to respond to surveys and what, if any, circumstances will increase the likelihood they will respond. Using a quantitative content analysis, relevant literature from three separate LIS databases – Library Literature & Information Science Full Text (EBSCOhost), Library, Information Science & Technology Abstracts (EBSCOhost), and Library & Information Science Abstracts (ProQuest) – is currently being gathered and evaluated. Preliminary findings indicate trends regarding LIS research purposes, methodology, and subjects. Il est bien établi que la méthode de recherche la plus utilisée par les bibliothécaires est l’enquête. Compte tenu de ce fait, cette étude en cours cherche à identifier exactement dans quelle mesure les bibliothécaires sont susceptibles de répondre aux sondages et quelles circonstances, le cas échéant, augmenteront la probabilité qu'ils y répondent. À l'aide d'une analyse quantitative du contenu, la littérature pertinente provenant de trois bases de données LIS distinctes - Library Literature & Information Science Full Text (EBSCOhost), Library, Information Science & Technology Abstracts (EBSCOhost), et Library & Information Science Abstracts (ProQuest) - est en cours de collecte et d'évaluation. Les résultats préliminaires indiquent des tendances concernant les objectifs, la méthodologie et les sujets de la recherche en bibliothéconomie et sciences de l'information.
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 | 0.096 | 0.369 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".