How the FAR Model Encourages Shift in Depth, Lift in Challenge, and Collaboration
Bibliographic record
Abstract
Chapter 4 covers the three underpinning principles of the FAR Model. The rationale for ‘shift’ in depth and ‘lift’ in challenge is offered, followed by detailed elaboration of ‘authentic collaboration’ as a central feature of effective goal pursuit. ‘Shift’ in depth can lead to greater focus and enhanced outcomes and impact. Depth is initially created via deep goal pursuit plan construction that essentially mirrors a mini AR approach with phased activity. ‘Lift’ in goal pursuit is associated with enhanced performance and outcomes — the higher the goal, the higher the performance. Lift in goals occurs when stretch, or challenge, goals are set rather than easy to achieve goals. Authentic collaboration is the underpinning which I consider to hold more significance to success than any other element. I attempt to show not only the defensive strategies and values preventing authentic collaboration but also what can be ‘productively’ implemented to enhance it.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".