The methods of shared concern: A positive approach to bullying in schools
Bibliographic record
Abstract
The Method of SharedConcern: A Positive Approach to Bullying in Schools by Ken Rigby Victoria, AU: Australian Council for Educational Research, 2011, 152 pages. ISBN: 9781742860077 (paperback) Fuelled by news media coverage of school violence, the topic of bullying has enjoyed tremendous attention in educational scholarship, professional development for pre-service and in-service teachers, and school administration over the past several years. Articles, books, programs, policies, scholarly articles, and experts abound. Arguably, bullying has become an over-researched topic. As one school administrator told me a few years ago, the last thing schools need is yet more research on When I received Ken Rigby's The Method of Shared Concern: A Positive Approach to Bullying in Schools (2011), I viewed it with a healthy dose of skepticism, despite knowing that Rigby is one of the world's foremost researchers on The Method of Shared Concern describes a multistage process by which a trained practitioner interviews suspected bullies and targets of Individual interviews lay the groundwork for eventually bringing all parties together to propose solutions that are offered by the students rather than imposed upon them by the practitioner. Rigby argues that the emphasis of anti-bullying efforts must be to promote positive interpersonal relationships (p. xiii). Based on a program developed by Swedish psychologist Anatol Pikas, the method is employed in several Anglo-Western countries around the world, including Canada. The book is not aimed toward scholars, but rather toward teachers and school counsellors. It is written in clear, accessible language, and is organized usefully into three broad sections. Part 1 explores what is generally meant by terms whose meanings are often taken for granted, such as bullying. Rigby suggests that bullying is a systemic and intentional abuse of power in interpersonal relationships. He cites Canadian scholars Debra Pepler and Wendy Craig, whose work explores the notion of bystanders. Pepler and Craig received a New Initiatives grant through the federal government's Networks of Centres of Excellence to create a national and interdisciplinary anti-bullying initiative called Promoting Relationships and Eliminating Violence (PrevNet.ca). Broadly put, they assert from their research that most students who witness bullying do not intervene. Like Pepler and Craig, Rigby focuses on fostering healthy interpersonal communication and dynamics. The second section of the book describes how the method works. Specifically, Rigby outlines the purpose of individual meetings and how they should unfold, and the possible pitfalls that could undermine success. For example, instead of teaching and preaching, practitioners should strive to learn how the suspected bully views the situation. Doing so requires employing skills of active listening, such as asking questions that clarify and probe, and paraphrasing to confirm understanding. …
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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 | 0.108 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.016 | 0.103 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.006 | 0.019 |
| 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; 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".