A scoping review about conference objectives and evaluative practices: how do we get more out of them?
Bibliographic record
Abstract
Large multi-day conferences have often been criticized as ineffective ways to improve social outcomes and to influence policy or practice. Unfortunately, many conference evaluations have also been inadequate in determining the impact of a conference on its associated social sector, with little evidence gathered or analyzed to substantiate or refute these criticisms. The aim of this scoping review is to investigate and report stakeholders' objectives for planning or participating in large multi-day conferences and how these objectives are being evaluated. We conducted a scoping review supplemented by a small number of key informant interviews. Eight bibliographic databases were systematically searched to identify papers describing conference objectives and/or evaluations. We developed a conference evaluation framework based on theoretical models and empirical findings, which structured the descriptive synthesis of the data. We identified 3,073 potential papers for review, of which 44 were included in this study. Our evaluation framework connects five key elements in planning a conference and its evaluation (number in brackets refers to number of themes identified): conference objectives (8), purpose of evaluation (7), evaluation methods (5), indicators of success (9) and theories/models (8). Further analysis of indicators of success identified three categories of indicators with differing scopes (i.e. immediate, prospective or follow-up) as well as empirical links between the purpose of evaluations and these indicators. Conference objectives and evaluations were largely correlated with the type of conference (i.e. academic, political/governmental or business) but diverse overall. While much can be done to improve the quality and usefulness of conference evaluations, there are innovative assessments that are currently being utilized by some conferences and warrant further investigation. This review provides conference evaluators and organizers a simple resource to improve their own assessments by highlighting and categorizing potential objectives and evaluation strategies.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Metaresearch Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | MetaresearchScholarly communication Domain: Evaluation · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.439 | 0.687 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.045 | 0.049 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.028 | 0.036 |
| Open science | 0.008 | 0.012 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".