Bibliographic record
Abstract
Alan Bryman and James J. Teevan, Social Research Methods, Canadian Edition. Oxford University Press, 2005, 409 pp. There are a number of research methods available to social scientists and Bryman and Teevan offer insight into the issues behind many of them. Unlike most methods texts, theirs highlights the importance of a number of research decisions rather than advocating a rigid set of methods. They also offer critical engagement of common assumptions and practical advice on navigating them. The authors' discussion of social research methods takes readers on a journey through various steps of the research process; beginning with a discussion of theoretical motivations and their effects on research decisions, followed by a balanced account of the strengths and weaknesses of quantitative, qualitative and mixed methods. They also include discussions of emerging web based methodologies and advice on how to use statistical and qualitative software packages to process information. In addition, the text provides dialogue boxes with definitions of key terms, tips, ethical issues, case studies and things to consider when writing up and presenting findings. Throughout, Bryman and Teevan remind readers that theory and practice are interrelated, noting the link between epistemological and ontological decisions with research design and practice. They introduce approaches influenced by natural science epistemology, such as positivism and realism, and juxtapose them against interpretive perspectives like Marxism and phenomenology. They also highlight how each is linked to differing ontological views, for example objectivism versus constructionism, and note how all of this correlates with deductive and inductive research designs, as well as leanings towards quantitative or qualitative research methods. The section dealing with quantitative approaches spends much time talking about survey research, going into detail on issues of structured questionnaires and interviews. For instance, these chapters offer insight into measures of reliability and inter and intra observer consistency. Bryman and Teevan also include a chapter dealing with other sources of data including government and official documents as well as the strengths and weaknesses of secondary data analysis. Chapters on qualitative methods introduce readers to ethnography and participant observation, which the authors treat synonymously, as well as unstructured, semi-structured and life history interviewing. There is only brief engagement of focus groups and little mention of comparative historical methods. Much of the discussion in these chapters contrasts the epistemological and ontological decisions made by researchers using these methods over quantitative approaches. …
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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 | 0.054 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.026 |
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".