A close encounter with ghost-writers: an initial exploration study on background, strategies and attitudes of independent essay providers
Bibliographic record
Abstract
Academic dishonesty presents in different forms, including fabrication of data, falsifying references, multiple submissions, collusion, and sabotage, with two forms haunting academia, namely plagiarism and contract cheating or ghost writing. These latter forms have received considerable attention and have been subjects for research. This interview-based study provides some further insight into the problem of ghost writing through presenting the attitudes, justifications and networking practices of some hired ‘ghost-writers’ from a developing country and discusses the depth of this emerging threat to the academic community. Initially, through simple internet searches using specific keywords, an array of professional advertisements selling contract writing services were identified. Some of these promotional advertisements were found in Facebook® posts, and/or Twitter® feeds. The second part of this study presents a summary of findings from interviews of a group of ghost-writers including their background, attitude and justifications for setting up this new business. The study identifies several high calibre post-graduates who have come to understand the Western (European/North American/Australian) ways of scientific writing and have produced a network of ‘consultancy’ services. Although the birth of their business was ad-hoc, they have established a good network and are now able to share projects and practices. Many of them offer services to home and foreign students with varied levels of customer focus. Some of them are even using Turnitin © software to identify text matching issues. This study suggests that these paper mills have widely been subscribed to by students. The article finally discusses wider issues arising from these interviews and proposes some ways of tackling this new threat to academia.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".