Replication Data for: Love Data Week in the time of COVID-19: A content analysis of Love Data Week 2021 events
Bibliographic record
Abstract
The analyzed data, README file, and codebook to reproduce the results and two charts from the related publication. Analyzed data is in two formats: original (Excel format) and .csv/archival format. Version 2 of this dataset involved adding metadata, adding the csv/archival format of the dataset, updating this description, and adding the README file. The analyzed dataset (Data_Analyzed sheet in the dataset) is composed of observations (each Love Data Week 2021 event) and the reconciled codes (i.e. the final codes assigned to each observation after 3 independent coders individually coded each event using the codebook and then discussed and reconciled any disagreements). Multiple codes could be assigned to each observation. We also coded for the name of a tool (e.g. "Excel" or "R") in separate columns. The codes apply to the event description and event title (variable names "Title" and "Description", columns B and D in this file). Column C (event type, i.e. "Type") was coded separately. See the README file for more detailed information about each sheet and documentation related to variable names/column headers.
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 | 0.008 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.060 |
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".