Technology in the Pandemic: Rupturing the Aura of Higher Education
Bibliographic record
Abstract
Framed by Benjamin’s (2008) concepts of aura, technological reproduction, and miniaturization, this paper argues that technology provides students with greater opportunities to take control of their learning in ways that were not possible when education took place largely on-campus, in the higher education classroom. Post pandemic, digital technology has influenced, and will further impact, the way in which we teach and communicate with our students. Educators emerging from pandemic pedagogies need a deeper understanding, and more judicious and effective use of technologies for learning. By understanding and using technology in these ways, educators can help students become more empowered, collaborative, and critically reflective. A lack of readiness for emergency online learning has been replaced with a sense of acceptance that educators must incorporate digital tools into their practice for both educational and communicative purposes. This means that our intellectual and social attachment to the physical classroom as a place for learning and communicating is not as strong as before. Far from being a eulogy for the physical classroom, this paper aims to interrogate the proliferation of technology, and the broad implications for higher education. It argues that the use of technology may help diminish the historical power and aura of higher education and intellectual pursuits, thus undermining the traditional overt and covert control of the institution as an ideological state apparatus. Nevertheless, this shifting educational landscape brings new challenges and questions about the historical aura of higher education, and the concomitant evolution of teacher student relationships and power structures through technology implementation. While maintaining optimism about educational technology, this paper urges educators to consider the broader context of our educational settings, and argues for prioritising critical engagement with technology that aims to utilise its potential for collaboration, empowerment and critically reflective inquiry.
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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.056 |
| Scholarly communication | 0.019 | 0.027 |
| Open science | 0.001 | 0.025 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".