Bibliographic record
Abstract
The advent of a continuous innovation and improvement, particularly in respect of information and communications technology has had a huge impact on the quality of life and in education delivery is an open secret.The benefits from exploiting ICT in education are ernomous but there are abuses that have tarnished its use in education.The excellence ICT is attributed with has been put to the sword and will stretch this innovation to develop tools for redressing these predicaments.ICT is with us from the cradle to the grave.This research reviewed published information and confirmed the overwhelming importance of information technology in marketing education.ICT has revolutionalised learning by enabling easy access to information through online materials and learning has become more convenient as students can combine work and online learning to good effect.However, academics and students have found it easier to access materials and often fail to acknowledge the source thus distorting the originality of the source.Where the systems for guarding against plagiarism are not thorough there is widespread abuse of the learning process.The paper recommends stringent systems for checking against plagiarism and that work submitted should be current if not live so as to avoid students duplicating work from elsewhere and appropriate punishment meted.At the same time there is software for detecting cheating in education which has gone some way in detecting the culprits and that had had a significant effect in reducing the proliferation of education malpractices.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.066 | 0.006 |
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".