HISTORY AND ROLE OF INFORMATION SECURITY IN POSTAGE EVIDENCING AND PAYMENT
Bibliographic record
Abstract
Step-by-step, information security technology has enabled the transition of postage evidencing and payment security from reliance on people, manual processes and paper records to reliance on automated procedures, trusted remote data systems and cryptographic protocols. This evolution improved both the security and the convenience of postage evidencing and payment through postage metering and thus enabled effective access to postal products. Reset, the process of adding postage to the meter, changed from a visit to a post office and manual record keeping to communication with a data center to receive an authentication code with a subsequent automatic completion of the transaction. Verification of the authenticity of printed indicia changed from a forensic analysis to automatic cryptographic authentication. Finally, with the introduction of NIST standard FIPS 140–1 level 4 physical security requirements, manual inspection of meters that are compliant with this standard by postal officials is being replaced by online verification of their physical integrity and procedural accuracy. These improvements in their totality enabled a remarkable transition of one of the most traditional office devices from the analog to the digital age.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".