The Chemistry of Mobile Phones: A Research Report on the Extent of Usage of the Compact Technology among Students on Nigerian Campuses
Bibliographic record
Abstract
When many authors were referring to radio and television as ‘new media’ some years ago, little did they realize thata group of media, will later emerge that will sooner be termed ‘newer’. The new development is about the adoptionof mobile phones. These trends have emerged in many social contexts including participation in social networks,changes in the traditional communication habits and exhibition of unanticipated behaviours resulting from mobilecommunication. Nigeria, like every other nation is not relenting her efforts in moving with time. When mobilephones first came into Nigeria, many felt it will wipe off the telecommunications company which was statutorily onground, today, the technology is making life easy for young and old.This study is an addition to the relevant literatures on media research especially the accommodation of ubiquitoustechnologies on Nigerian campuses. It will draw attention to the ways Nigerian students use mobile phones andinvestigate attitudes about mobile phone usage in public settings. Although, it might look as if nothing new is beendescribed that is not common in other settings, this seem to be the first time that the youth will take the lead in theadoption of an innovation in Nigeria, leaving the trend unexplainable to the adults.It started with a discussion on the features of phones and moves to present some literature relevant to mobile phoneusage. The study found out that students make use of phones everywhere including restricted spaces like bankinghalls, cars and lecture rooms. Some confessed to causing accidents on campus because they were engaging phoneswhile driving. The study however recommends educational discussions on mobile phones to enhance students’positive and moderate usage of 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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".