Employers’ perspectives on new information technology technicians’ employability in North Florida
Bibliographic record
Abstract
Purpose In response to recent calls for research relating to employers’ perceptions of the workplace readiness of new graduates in a variety of fields, the purpose of this paper is to report North Florida employers’ perceptions of information technology (IT) program graduates’ workplace readiness. These findings are relevant to stakeholders in growing technology regions. Design/methodology/approach Researchers conducted 18 semi-structured interviews with IT employers in North Florida. Data were deductively coded with codes derived from national standards. Interviewee verbatim was also inductively coded by theme. Findings While employers valued a blend of technical and general skills and hands-on experience, they also sought new professionals who possessed fundamental understandings of business and computer programming to tailor their problem-solving skills to the specific company environment. Research limitations/implications This research represents a limited number of employer viewpoints in one representative community. Practical implications Ongoing industry input into curricula and expanded experiential opportunities may ensure that graduates are prepared to address current and future IT developments. Because the region under study was typical of many regions with growing technology sectors, these findings may inform partnerships, curriculum, and program design. Originality/value Given the rapid growth and constant advances of the IT sector, institutions with IT degree programs are challenged to ensure that their curricula are current and meeting the needs of employers. This study’s findings may offer timely insight into elements of workforce preparedness.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".