Towards a new construct of social, emotional and behavioural difficulties
Bibliographic record
Abstract
The socio-economic context of SEBD teacher-research and practice-based interventions – in inner-city state schools predominantly– means that the literature in this field has often given disproportionate predominance to the ‘social’ element in social, emotional and behavioural difficulties (SEBD). Though seeking a ‘cause’ is not a determining feature of SEBD literature, it is nevertheless assumed in most literature that emotional instability and behaviour manifestations are usually loosely and intrinsically linked to social status and, to some extent, to economic difficulties and their complex ramifications. This paper will examine a decade of teacher-research analysis compiled within a private sector school and attempt to examine hypotheses that to some extent take the emphasis away from the ‘S’ in SEBD. The data which were collected in a residential school, situated in Quebec, during a period of 12 years between 1997 and 2009, will show that students with SEBD are frequently in fact encountered in the private sector even if, for administrative reasons, they are handled differently and often not given the SEBD label. If social disparities and economic difficulties are in fact not predominantly relevant in manifestations of SEBD, how then can assumptions be reversed and a different construct be put forward to explain why certain children, regardless of background and socio-economic context develop SEBD? Since many of the data collected show that these students may later develop into adjusted adults. The conceptual looking glass of the medical model must be discarded as being incapable of explaining the universal manifestation of SEBD through the socio-economic spectrum. The hypothesis put forward in this paper is that children with SEBD should be seen, foremost, as children unable or unwilling to make a socially acceptable distinction between ‘private’ world and ‘social’ context. For reasons unknown, they refuse to comply with or fail to recognise the ‘Chinese walls’ which exist between these two aspects of human existence and which must generally be adhered to achieve ‘functional’ status. The study draws on documents relating to and data collected from three cohorts, aged 16 to 19, over a period of 12 years.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.004 | 0.049 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".