Research 2.0: A Framework for Qualitative and Quantitative Research in Web 2.0 Environments
Bibliographic record
Abstract
The paper explores the potential of the Web 2.0 environment for conducting both qualitative and quantitative research. The paper analyzes the emerging Research 2.0 domain using the theoretical framework of Web 2.0 core principles (e.g., web as a platform, harnessing collective intelligence, etc.). These principles, first proposed by Tim O'Reilly, provide a useful lens through which researchers can examine the potential for Web 2.0 technologies in shaping the next generation of research methodologies. To this end, the paper examines how these principles would apply to the research domain, how traditional methodologies used in qualitative and quantitative research can be applied within a Web 2.0 environment, and the challenges and issues that researchers may face in a Research 2.0 domain. The paper identifies key research issues that need to be explored to fully realize the potential of Web 2.0 technologies in conducting qualitative and quantitative research.
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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 | 0.239 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".