“Prediction is Difficult, Especially the Future”: A Progress Report
Bibliographic record
Abstract
Objective - This paper reviews developments in the consolidation and diversification of the evidence based library and information practice (EBLIP) paradigm since publication of the authors’ book Evidence Based Practice for Information Professionals: a Handbook in 2004. Methods - The authors provide an updated narrative review of key themes in the development of evidence based librarianship within the context of the new consensual term ‘EBLIP.’ Sources for this thematic framework included professional literature, Internet searches, and the authors’ personal experiences. Results - While considerable achievements have been realized within a three-year period, most notably the instigation of the journal known as EBLIP, a broadening of the paradigm to other library sectors, and increased availability of implementation studies, many challenges remain. Of particular concern is the lack of international strategic foresight in determining rotation of the biennial international conferences and distribution of influential EBLIP infrastructures and initiatives. Conclusion - While the enthusiasms and energies of individual practitioners and work teams have made considerable progress in meeting short-term objectives, uncertainty remains concerning how longer-term objectives requiring infrastructure and resources might be realized. From its faltering steps as a toddler EBLIP has developed to a ‘pre-pubescent’ stage with the promise of ‘growth spurts’ and ‘emotional crises.’ The next three years should prove both challenging and demanding.
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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 | 0.035 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.012 | 0.028 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".