Techno Savvy and All-knowing or Techno-oriented?: Information-seeking Behaviour and the Net Generation?
Bibliographic record
Abstract
During the last twenty years rapid developments in technology have led to changes in the way we work, play and learn. Technology has become an integral part of society’s everyday landscape. Children growing up during what has been called the technological or digital revolution have never known a world without instantaneous communication and easy access to vast quantities of information delivered in multiple formats. For the ‘Net Generation’ of users and consumers, technology is transparent and a part of their social, economic and educational landscape. They are surrounded by information using a multitude of formats, text types, graphics and multimedia. Adult observers of these young people marvel at how they use and cope with a wide range of technologies, often seemingly oblivious to instruction manuals. The Net Generation already seem to have the skills to deal with the array of old and emerging technologies. The terms tech-savvy, web-savvy, Internet-savvy and computer-savvy are being used to describe young people in major educational policy documents and population studies worldwide. While educators recognise that their students have a different culture of use when using and seeking information delivered electronically, they struggle to come to terms with the changes the integration of technology brings to the teaching-learning environment. Teachers are continually being reminded that they are the ones who are being left behind a generation for whom the use of communications technologies appears to be intuitive. The question for researchers and educators is do students have an intuitive grasp of how to use electronic information or is this just an illusion borne of familiarity with the technology?
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 | 0.002 | 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 | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".