Portfolio Evaluation and Impact Assessment
Bibliographic record
Abstract
The purpose of this chapter is to introduce and explore the main event port- folio evaluation and impact assessment methods. The principles of financial portfolio management are discussed, considering their applicability to event portfolio evaluation, which should be done with caution, as events are not merely financial assets. The chapter highlights that the evaluation of event portfolios is complex, requiring new theories, methods and measures. To develop a comprehensive evaluation system, it is emphasised that there is a need for a multi-stakeholder approach to valuing event portfolios, considering both intrinsic values and extrinsic measures of worth. The chapter discusses four types of impact assessment and their application to portfolio evaluation. Key terms and concepts are explained, including value, evaluation, impact assessment, asset, outputs, and outcomes. The relevance of organisational ecology theory to portfolio evaluation is stressed. The nature and use of logic and theory of change models are examined followed by a discussion of portfolio strategy models and their relevance to evaluation. Finally, it is illustrated how to assess values against costs and risks within portfolios.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".