The case for companywide enterprise digital asset management
Bibliographic record
Abstract
Digital media management systems have become critical to the successful implementation of brand expression programmes and brand marketing campaigns. Marketing and IT leaders increasingly need these systems to become the single point of truth for media assets. They need enterprise digital asset management (DAM) to solve big problems around accessibility to media across platforms and channels. The successful management of platform access to media assets enables marketers to safeguard, leverage and reuse their most prized assets. Getting these actions right positions marketing campaigns for success and leads to an effective return on investment on these assets. This presumes that usage of the DAM system is good and consistent across all content contributors. Strong DAM adoption across the enterprise sets the foundation through which excellent marketing execution can be realised. This paper explores the goal of strong enterprise DAM adoption and takes a deep look at what this means and how it can be done in a highly variable and decentralised environment with an unsophisticated user community.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".