Bibliographic record
Abstract
A s the internet becomes more integral to all aspects of our lives, so- cial scientists continue to grapple with its potential for our research endeavors.While many senior scholars and practitioners learned to do research without the internet or even without computers, students and new colleagues come to research as digital natives who are often quite comfortable with online settings and activities.Salmons, an independent researcher, writer and consultant who specializes in online communities, entrepreneurship and leadership in the digital age, is attempting to bridge the gap between qualitative methods before and after the internet.Her previous books, Online Interviews in Real Time (Sage, 2010), Cases in Online Interview Research (Sage, 2012) and Qualitative Online Interviews (Sage, 2015), demonstrate her sustained interest in the possibilities of our new digital age.In Doing Qualitative Research Online, Salmons carefully outlines the questions and details researchers need to attend to in designing their online research [e.g., aligning purpose and design, choice of extant, elicited, and enacted data, selecting ITC, addressing ethical issues, sampling, etc.].She utilizes her concept of "Qualitative e-Research Framework" (Salmon 2012) to organize the eleven chapters and provide a 'road map' throughout the text.Salmon provides an overview of the methodologies, methods and ethics for doing qualitative research online, and explores three types of online data collection-extant, elicited, and enacted.Early on, she argues that researches should not just "repurpose real-world data collection techniques" for a virtual world, but should instead employ digital approaches that make use of "text-based exchanges (messaging, email), multi-channel meeting spaces (e.g.Adobe Connect, WebEx), videoconferencing (full video with multiple participants) or video calls (e.g.Skype, Google Chat) or immersive virtual worlds (e.g.Second Life, games) [which] are fundamentally different from real-world, co-located interviews and observations" (xiii).
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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 | 0.045 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".