Storytime, Audience to Authors: <i>Enhancing Stakeholder Engagement</i>
Bibliographic record
Abstract
Abstract This paper is largely aimed at Public Agencies, such as Infrastructure and Transit authorities. The creation and development of projects at these agencies impacts multiple layers of stakeholders, who, in the collective experience of the authors, are often not involved in the project until the middle or end of the development phase or not until they have to use the system. These neglected end users and influencers of the system do not have a timely voice – they are effectively excluded. This paper advocates for the early engagement of all defined stakeholders; the obvious and the unconventional, both internal and external to the agency. This paper does not provide a detailed ConOps process but rather defines what a ConOps is, why it is necessary, and at what stage in the project should one be developed. The principal conversation herein focuses on how to determine who the audience is for the system of interest (SOI) and ways to engage them. The paper describes a holistic approach to the creation and development of a ConOps deliverable by engaging the audience, who become stakeholders and effectively, authors. This paper concludes with a case study encompassing the discussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.023 | 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".