Longitudinal Tracking and Changes Over Time of Song-writing Workshops with Young People and Adults who are Experiencing Different Degrees of Social Exclusion
Bibliographic record
Abstract
Most funded organisations within the UK who run arts activities including those which are music related, evaluate the impact of their work by reviewing soft skills, and areas relating to well-being. On discovering that there is no official form of tracking for measuring outcomes within the UK, this presented the opportunity to explore five different measuring tools. Therefore, giving the scope to design, trial and implement a longitudinal tracking model focusing on an evaluation of the specific skills taught during workshops with particular references to changes over time. This led to producing a Model which stipulates targets for each stage of the process. The Model created for this research is the FiLTER Model; Framework in Longitudinal Tracking Experiential Reports. Described by the UK Government Department of Business, Innovation and Skills as a valuable methodology for measuring impact which has been a ‘longstanding concern’ within the criminal justice system (Hayes, 2011). Generally, the funding partner’s methods, evaluations and techniques do not promote or request evaluations based on a longitudinal framework. To trial the Model, I focused on song-writing workshops attended by participants experiencing different degrees of social exclusion. The accompanying tracking questionnaires are known as Specific Skills Checklists (SSCs). They provide an opportunity to ask participants during the measuring process to reflect on their specific skills gained and convey whether they had continued to use any of these, or indeed evaluate any changes which may have occurred over time. Due to the nature of the workshop environments, each of the four case studies produced only small samples. Despite certain challenges with using a measuring process over a period of time, the FiLTER Model designed worked well and the SSC questionnaires were returned. The content of these are flexible, and allow for the Model to be transferable for other arts activities. There is now evidence of impact with a third-party community arts organisation successfully using the FiLTER Model and discussions have begun with other organisations to encourage its use.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".