Bibliographic record
Abstract
Purpose Just like human beings, some companies engage in recurrent bad behaviour that negatively impacts their stakeholders and their prospects for long-term survival. For example, some firms become caught up in a vortex of corruption. SNC-Lavalin, a large Canadian consulting engineering company, is an example of one organisation that embarked on this path. Since then, the company has taken numerous steps to overcome its persistent problems with corruption. The object of this study is to determine whether these steps can be compared to the 12-step program of Alcoholics Anonymous (AA), which is recognised for helping individuals overcome addiction to alcohol and drugs. Design/methodology/approach To examine events at SNC-Lavalin between 2000 and 2022, the authors carry out an in-depth examination of internal and external documentation. Three sources of data are used: archival documents, news articles and corporate documentation. Findings The results of the analysis show that the AA 12-step program seems to correspond to the steps SNC-Lavalin has taken over time. The “organisational” version of this program that the authors have developed could be useful to advisers of companies that are struggling with other types of bad behaviour and wish to stamp it out. These bad behaviours include the exploitation of vulnerable manpower, the exploitation of consumers through planned obsolescence or aggressive sales practices and pollution in all its forms. Research limitations/implications The study has certain limitations. It should be noted that the analyses were limited to public information. In addition, given the quantity of public information available for the period from 2009 to 2022, a methodical approach to selecting the sources of information elements was applied, which inevitably entailed ignoring other sources of information (e.g. television, radio and internet). Originality/value This study adds to previous work by providing an original and global perspective of the steps taken by a large international consulting engineering firm to overcome its recurring corruption problems. The parallel drawn with AA’s 12-step programs seems to correspond surprisingly well to the steps taken by the company. This parallel can potentially serve as a roadmap for advisers who have to counsel companies on recurring misconduct that has harmful repercussions for their stakeholders.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".