Analysis of Textual data for integrating an automated coding environment system and building a system to monitor the quality of its results
Bibliographic record
Abstract
Actually Italian National Institute of Statistics (ISTAT) is evaluating the c hance of using a software for automatic coding of textual responses to questions about occupation, education level etc.. The system chosen is ACTR (Automated Coding by Text Recognition) developed by Statistics Canada. A first test of the system was carried out with data from the quality survey on Population Census of year 1991. The good results obtained led to p erform a further analysis with textual data from Labour Forces Survey. The purpose was to d efine a standardised p rocedure which to refer when ACTR is used d uring a survey instead o f a manual coding. In particular, the analysis carried out in this paper aims at developing a procedure to integrate the basic automated coding environment and to build up a system to monitor the quality of the results of automated coding.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".