The Instrumental‐Symbolic Framework: Organisational Image and Attractiveness of Potential Applicants and their Companions at a Job Fair
Bibliographic record
Abstract
This study investigates perceptions of organisational image and attractiveness among 200 potential applicants for the Belgian Defense and the person (e.g. friend, parent) accompanying them to a job fair. The instrumental‐symbolic framework is applied to conceptualise the key dimensions of an organisation's image as an employer. The results indicate that instrumental image attributes predict perceived organisational attractiveness for both potential applicants (social activities, structure, and advancement opportunities) and their companions (educational opportunities). Moreover, consistent with the instrumental‐symbolic framework, symbolic image traits explain incremental variance in the attractiveness perceptions of potential applicants (sincerity, excitement, prestige, and ruggedness) as well as of companions (sincerity and ruggedness). Overall, instrumental and symbolic image predict attractiveness more strongly for potential applicants than for their companions, and potential applicants have a somewhat more positive view of the organisation. In addition, companions' perceived attractiveness positively predicts potential applicants' attractiveness beyond potential applicants' instrumental and symbolic image perceptions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".