The Handbook of Online and Social Media Research: Tools and Techniques for Market Researchers
Bibliographic record
Abstract
Drawing together the new techniques available to the market researcher into a single reference, The Handbook of Online and Social Media Research explores how these innovations are being used by the leaders in the field. This groundbreaking reference examines why traditional research is broken, both in theory and practice, and includes chapters on online research communities, community panels, blog mining, social networks, mobile research, e-ethnography, predictive markets, and DIY research. This handbook fills a significant learning gap for the market research profession and Ray Poynter has once again proven that he is a guiding light. The practical and pragmatic advice contained within these pages will be relevant to new students of research, young researchers and experienced researchers that want to understand the basics of online and social media research. Rays views on how to be better with people and how to maximise response rates are vital clues that are likely to shape the future of market and social research. Peter Harris, National President, Australian Market and Social Research Society (AMSRS). Its hard to imagine anyone better suited to covering the rapidly changing world of online research than Ray Poynter. In this book he shows us why. Whether you are new to online or a veteran interested in broadening your understanding of the full range of techniquesquant and qualthis book is for you. Reg Baker, President and Chief Operating Officer, Market Strategies International Finally, a comprehensive handbook for practitioners, clients, suppliers and students that includes best practices, clear explanations, advice and cautionary warnings. This should be the research benchmark for online research for some time. Poynter proves he is the online market research guru. Cam Davis, Ph.D., former Dean and current instructor of the online market research course for the Canadian Marketing Research and Intelligence Association Ray Poynters comprehensive, authoritative, easy to read, and knowledgeable handbook has come to our rescue ... it is a must read for anyone who needs to engage with customers or stakeholders in a creative, immediate and flexible way that makes maximum use of all the exciting, new technology now open to us. Market researchers need to know this stuff now. I can guarantee that anyone who buys the book will find it a compelling read: they will be constantly turning to the next page in order to find yet another nugget of insight from Rays tour de force. Dr David Smith, Director, DVL Smith Ltd; Professor, University of Hertfordshire, Business School
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.013 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.041 |
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".