Bibliographic record
Abstract
Organizations invest in digital information technologies (IT) to create value for the organization and its stakeholders. They do not (and should not) do it to be up-to-date with the latest technology or to create more interesting work for IT professionals. Shareholders, customers, citizens, and donors put money into organizations to get some value out of them. This is a vital truth that must be understood by people in IT as well as those in other parts of the organization. For business value to be derived from IT investment, it must be possible to clearly articulate what that value is. It cannot be a vague concept that is not measurable. Value must be measurable and must also be measured in practice. Only then can there be accountability for the results as well as learning for continuous improvement. For our purposes, value is the agreed-upon benefit to be derived from applying IT to support the delivery of outcomes customers are willing to pay for or fund . (Customers, in this instance, is used as a generic term to refer to clients, constituents, donors, voters, and other stakeholders for whom value is being created and delivered.) Profit resulting from commercial activity by business firms may be one measure of business value. Other measures could include outstanding public service delivery (such as clear roads in winter, faster ambulance or fire response times) by a municipal government, significant reduction in medication errors in a hospital, or increase in the number of meals served by a not-for-profit or charitable organization. If value is not perceived by customers, they will not pay for or fund it over the long term. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.019 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".