Bibliographic record
Abstract
Introduction The online research environment has changed somewhat dramatically over the decade 2000–10, and researchers are now more connected than ever before both to their fellow researchers and to an unprecedented amount of information. It is now possible to ‘watch’ research as it happens – and in some cases to participate in its development – as one can follow the blogs of well known researchers, receive RSS updates from websites with a research focus, subscribe to podcasts, participate in the creation of wikis and in other collaborative environments, share citations and other bookmarks and join social networks around topic-specific research interests. All of these activities fall under the heading of the so-called Web 2.0, a suite of tools and technologies that has changed the way we interact with and use the web. In an early article on Web 2.0 and medicine, Giustini notes that ‘the more we use, share, and exchange information on the web in a continual loop of analysis and refinement, the more open and creative the platform becomes, hence, the more useful it is in our work’ (Giustini, 2006, 1283). As in the past, the central problem of conducting good research is to stay abreast of developments within a field of study. Healthcare and biomedicine are areas where new developments occur in rapid succession and leaps in discovery happen. Thus, the need to stay as networked as possible and to be ‘tuned in’ to new developments and discoveries is essential. Web 2.0 tools and technologies offer many options for accomplishing this goal. In addition, the pathway from primary research to knowledge translation to discovery by clinicians has been greatly expedited by these tools. As Hughes et al. note in their 2009 study of the junior physician's use of Web 2.0, ‘although credibility of information was the most cited concern, tools such as Wikipedia or Google are used 3 times more … than PubMed, the “official” best evidence tool introduced in medical school’ (Hughes et al., 2009, 651). Thus, one must choose wisely in the realm of Web 2.0 and use good information literacy skills in evaluating content, identifying its provenance and placing it in context.
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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 | 0.017 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.019 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.745 | 0.596 |
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".