ICT skill frameworks: do they achieve their goals and \nusers’ expectations?
Bibliographic record
Abstract
Objective: To examine whether existing ICT skill frameworks achieve their goals and the expectations that end users may have. \n \nMethods: First we examine typical objectives and user expectations of ICT skill frameworks. Then three existing ICT skill frameworks, specifically SFIA, e-CF and SF for ICT, are surveyed and compared with each other in terms of their design choices and feature sets. The implications of some of these design choices are discussed, particularly where there are significant differences between the frameworks or where there are apparent conflicts with objectives or user expectations. We also identify salient features which are missing from all existing frameworks. \n \nResults: The existing frameworks differ in a number of significant areas, including the number of hard skills and the treatment of soft skills. Furthermore, all three frameworks surveyed might be considered somewhat complex in terms of defining skill proficiency using multiple attributes and the intricacy of the skill/proficiency mapping. There is also a lack of unambiguous and universal certification criteria, which limits the portability of the frameworks between organisations. Finally, automation of skills management is also hindered by the fact that the skills are defined in natural language without any specific structure or semantics that could be leveraged by advanced applications. \n \nConclusions: The significant differences between and the complexity of existing ICT skill frameworks implies that debate is still required about how an ICT skill framework should be designed to be of maximum utility. Existing frameworks need to be extended or complemented to support important use cases around portability and automation.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".