Achieving Transparency, Traceability, and Readability with Human-Coded Data
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 Many important questions in political science require the use of human-coded data or information that has been systematically ordered and quantified by a human being from qualitative sources. This article discusses challenges and recent innovations in collecting and documenting human-coded data. We review five datasets produced within the past 10 years and also reflect on our experiences in collecting a quarterly dataset that tracked state responses to the COVID-19 pandemic. We argue that scholars can deliberately produce and publish theoretically grounded human-coded data in an accessible format that promotes transparency, traceability, and readability. We highlight several ways that scholars are already doing this, including narratives, source lists, and coding justifications that enhance the quality of their human-coded datasets. We also discuss common issues during coding and how technological innovation through interactive web-based platforms can improve the documentation of coding decisions.
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.
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.001 | 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