Bibliographic record
Abstract
Leading technology integration in higher education requires an inquisitive, reflective approach. This chapter discusses key questions that university administrators, policy makers, faculty, and other stakeholders must address in order to effectively integrate technology into higher education. The questions are divided into three categories. First order question are conceptually simple questions that can be answered with basic information and little controversy. First order questions primarily relate to the cost, availability, and capabilities of technology. Second order questions build on first order questions and require more data, greater participation, and deeper analysis to be effectively answered. Examples of second order questions include how to effectively implement technology, the costs and benefits of technology, the unintended consequences of technology, and how to move from operational to strategic planning. Third order questions involve the most complex, controversial, and profound issues of technology and higher education. These questions will likely never be definitively answered but force us to continually reassess and evaluate our fundamental beliefs about higher education. Third order questions relate to the role of higher education in society, the control and ultimate impact of technology, and how technology affects the essential elements of the higher education experience.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".