Career and Community Possible Selves: How Small-town Youth Envision Their Futures
Bibliographic record
Abstract
This thesis examines the way in which youth between the ages of sixteen and eighteen envision their future possible selves with respect to their possible careers and roles in the community. The youth were recruited from members of the Fusion Youth Activity and Technology Centre (a.k.a. Fusion) in Ingersoll, Ontario; population 12,146 (Statistics Canada, 2011). Situated in the context of small-town youth who attend afterschool activities aimed at providing skills in business, the arts, media and technology, the study asked youth to consider what their future possible selves would look like ten years from now. Using Q-methodology, the participating youth were asked to complete a 55-statement Q-sort with statements relating to careers and community roles generated by a focus group of Fusion youth and from the relevant literature. Using identical statements, the sort was conducted under two conditions of instruction; thinking of your hoped-for self in the future and; thinking of your feared self in the future. Factor analysis was conducted on both sets of Q-sorts (hoped-for and feared) and three factors were extracted for each. In keeping with Q-methodology, composite sorts were generated giving three distinct profiles of statement placement for each of the hoped-for and feared selves. Hoped-for profiles included community-minded professionals, independent creatives and no-plan dreamers. Feared self profiles included, disengaged problem citizens, trapped labourers and unhappy average citizens. These six different viewpoints of their possible futures indicate that youth see their futures (both good and bad) very differently and that their career foci and community involvement hopes and fears are far from homogeneous. This opens an opportunity for youth programs like Fusion to develop programming specific to these groups that may help to make hoped-for selves the more probable outcome.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".