The impact of survey data: Measuring success
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.
Bibliographic record
Abstract
Abstract Large national social surveys are expensive to conduct and to process into usable data files. The purpose of this article is to assess the impact of these national data sets on research using bibliometric measures. Peer‐reviewed articles from research using numeric data files and documentation from the Canadian National Population Health Survey (NPHS) were searched in ISI's Web of Science and in Scopus for articles citing the original research. This article shows that articles using NPHS data files and products have been used by a diverse and global network of scholars, practitioners, methodologists, and policy makers. Shifts in electronic publishing and the emergence of new tools for citation analysis are changing the discovery process for published and unpublished work based on inputs to the research process. Evidence of use of large surveys throughout the knowledge transfer process can be critical in assessing grant and operating funding levels for research units, and in influencing design, methodology, and access channels in planning major surveys. The project has gathered citations from the peer‐reviewed article stage of knowledge transfer, providing valuable evidence on the use of the data files and methodologies of the survey and of limitations of the survey. Further work can be done to expand the scope of material cited and analyze the data to understand how the longitudinal aspect of the survey contributes to the value of the research output. Building a case for continued funding of national, longitudinal surveys is a challenge. As far as I am aware, however, little use has been made of citation tracking to assess the long‐term value of such surveys. Conducting citation analysis on research inputs (data file use and survey products) provides a tangible assessment of the value accrued from large‐scale (and expensive) national surveys.
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 arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it