How paradata can illuminate technical, social and professional role changes between the Poverty in the UK (1967/1968) and Poverty and Social Exclusion in the UK (2012) surveys
Bibliographic record
Abstract
This article brings together analyses of the micro paradata 'by-products' from the 1967/1968 Poverty in the United Kingdom (PinUK) and 2012 Poverty and Social Exclusion in the UK (PSE) surveys to explore changes in the conditions of production over this 45 year period. We highlight technical, social and professional role continuities and changes, shaped by the institutionalisation of survey researchers, the professionalization of the field interviewer, and economisation. While there are similarities between the surveys in that field interviewers were and are at the bottom of the research hierarchy, we demonstrate an increasing segregation between the core research team and field interviewers. In PinUK the field interviewers are visible in the paper survey booklets; through their handwritten notes on codes and in written marginalia they can 'talk' to the central research team. In PSE they are absent from the computer mediated data, and from communication with the central team. We argue that, while there have been other benefits to field interviewers, their relational labour has become less visible in a shift from the exercise of observational judgement to an emphasis on standardisation. Yet, analyses of what field interviewers actually do show that they still need to deploy the same interpersonal skills and resourcefulness to secure and maintain interviews as they did 45 years previously.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".