Effective Poster Teaching Strategy towards Risk in Studying Fraud
Bibliographic record
Abstract
The aim of this paper is to present an alternative method and strategy in teaching and learning for the higher institution of learning. Poster presentation is an approach to introduce and deliver a lecture to create a different mood enticed by the visuals given. This poster presents a new approach of creativity as a method of teaching and learning in a classroom. The course sampled was risk management. The whole idea is to make the teaching presentation interesting by using visuals i.e. an instructive poster. Each ‘point’ on the poster means a thousand words. The presentation shows how risk management is encompassed by knowledge, understanding and controls of forensic accounting and financial criminology. It is self-explanatory and acts as an animation in itself. Risk management is seen to be an umbrella, shielding away entities from the unpredictable environment and weather i.e. malpractices, corporate failures and frauds which are now rampant across the world. A formal education and an academic qualification are now a necessity to combat these hitches. This is accomplished by exposing the ‘gate of thumbs’ as labeled and arranged under the umbrella, signifying a study of forensic accounting and financial criminology through various courses. Research is then undertaken to express and instigate feasible topic as a test of further understanding. As shown on the poster, ‘with a command of English to report, if ‘…it takes a thief…’ then in this study, it takes one function to deter fraud, i.e. risk management; and three levels to hook fraud, namely forensic accounting, varied courses related plus a level of research work.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.090 | 0.025 |
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".