Internet Communications Technology Skills and Systems of Engagement
Bibliographic record
Abstract
The Internet communications technology (ICT) worker of tomorrow will need skills that address the problems of today’s enterprises. This article outlines changing attitudes in project management, their relationship to decision making, and the role of the ICT worker. Large enterprises have traditionally relied on plan-driven project management that presents a clear view of how to engineer and design a product for market. In this view, engineering is “building the thing right” and design is “building the right thing.” Plan-driven models succeed or fail on a top-down ability to define what right means in either context. In today’s market, speed is everything, to the point that many see plan-driven project management failing to keep up with the pace. Other approaches, such as the agile approach, are gaining momentum but focus mostly on reducing time to market while relying on user feedback to define what right means. Who then is best suited to know what right means? Executives at the head of corporate hierarchies, or the people who interact with technology as users? The ICT skills needed to address this question will require a balance between speed and judgment. Participatory governance studies can inform ways to increase speed through devolved decision making, and design ethnography can provide ways that devolved decision makers can cultivate good judgment about their users. We outline this theory in practice with three case studies: delivering educational programs for app development, attempting to broaden civic engagement with natural language processing algorithms, and facilitating Indigenous clean energy projects with design thinking practices.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".