Bibliographic record
Abstract
Information and communications technology (ICT) has been changing rapidly over a long period and this rate of change is likely to continue or increase (Benamati & Lederer, 2001a; Lee & Xia, 2005). This rapid rate of change has produced many opportunities for organizations, but has also brought with it many challenges (Benamati & Lederer, 2001b). Among these challenges is the struggle for organizations to obtain personnel with the appropriate information technology (IT) knowledge and skills in order to meet their ICT needs (Byrd & Turner, 2001; Doke, 1999; Standbridge & Autrey, 2001). This is mirrored by the continual requirement for IT professionals to keep up to date with the skills required by organizations (Benamati et al., 2001a; Klobas & McGill, 1993; Moore, 2000). Previous research has investigated the importance employers place on various skills and perceived deficiencies in these skills (e.g., Doke, 1999; Leitheiser, 1992; Nelson, 1991; Prabhakar, Litecky, & Arnett, 2005). While the call for improved communication and social skills has been consistent, the technical skills in demand have varied dramatically over time (Prabhakar et al., 2005; Van Slyke, Kittner, & Cheney, 1998). Less has been written about students’ perceptions of the importance of various ICT skills, though this was addressed in a study that compared Australian and American students’ perceptions of ICT job skills (von Hellens, Van Slyke, & Kittner, 2000). This article provides an overview of a project that investigated the channels of information that ICT students use to keep up to date with employers’ needs.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".