Is technical demography becoming less relevant? Two decade review of published articles in selected demography journals
Bibliographic record
Abstract
Background: In this paper, we reviewed development in the field of technical demography and empirically demonstrate that there has been a decline in the proportion of technical demographic studies published in the last two decades.Methods: All original articles published in nine demographic journals from Africa, Europe, Australia, Canada and United States were reviewed. We derived yearly aggregate for total number of articles and number of technical demographic papers from 1994 to 2015. We illustrated the trends in the proportion of technical demographic studies in a graph and also estimated the annual rate of decline using least square regression techniques.Results: A total of 4091 studies were published in 465 issues of the selected journals between 1994 and 2015 of which 371 (9.0%) were related to technical demography. The proportion of technical demographic papers declined gradually at an annual rate of 0.42% (CI= 0.29-0.62) between 1994 (12.0%) and 2015 (10.0%).Conclusion: Technical demography need to be strengthened in order to provide the critical data and evidence required to objectively monitor the post-2015 development goals.
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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 | 0.016 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.033 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".