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4.1 · Evidence — the intellectual-assets analysis
The instrument behind the capability claim in 1.1.2. It measures what the technical staff were capable of; it is deliberately not offered as evidence that the organisation was dysfunctional.
4.1.1 · What the measurement was
Window. Roughly six to eight years before this was written. The exact span is left out on purpose, and nothing in the argument turns on it.
Population. Everyone on the technical staff employed by the division at any point during that span — not everyone employed on a single fixed date. Two strata:
- Group A — scientists and engineers (control, mechanical, data science): n = 14
- Group B — software developers: n = 9
- Total: n = 23
Coding rule. Each person was assigned their current position, then classified:
| Class | Definition |
|---|---|
| Notable exit | Left, and holds a mid-to-high-level position judged against international academic or industrial standards |
| Other exit | Left, position not meeting that bar |
| Retained | Still with the division |
For Group B the bar was set differently and deliberately: securing an international developer role from inside a sanctioned labour market was itself treated as the notable threshold, on the reasoning that the filter is severe independent of the role’s seniority.
4.1.2 · Results
| Notable exit | Other exit | Retained | n | |
|---|---|---|---|---|
| Group A — scientists & engineers | 7 (50%) | 3 (21%) | 4 (29%) | 14 |
| Group B — software developers | 4 (45%) | 3 (33%) | 2 (22%) | 9 |
| All technical staff | 11 (48%) | 6 (26%) | 6 (26%) | 23 |
Read plainly: of everyone on the technical staff during the window, roughly half now hold positions that are internationally competitive.
4.1.3 · Destination inventory
Destinations are reported as an inventory of categories with counts. Individual rows are not published: with n = 23, a row combining a specialism, a destination region and a departure window identifies a person to anyone working in the field.
Notable exits (11)
| Destination category | Count |
|---|---|
| Top-ranked international research universities (US, Australia, New Zealand) | 5 |
| European research laboratory in vibration and acoustics, with major industrial partnerships | 1 |
| R&D role at a multinational energy-technology corporation | 1 |
| Data-science role at a US technology startup | 1 |
| International software-engineering roles secured from within the domestic market (Europe, Canada) | 3 |
Other exits (6)
| Destination category | Count |
|---|---|
| North American research universities | 2 |
| Doctoral / postdoctoral position, Europe | 1 |
| Entry-level specialist role, Canada | 1 |
| Domestic industry, other employer | 1 |
| Departed, destination not recorded | 1 |
Retained (6) — still with the division at the time of the analysis.
4.1.4 · Notes on the coding
The classification is deliberately conservative, so 48% is a floor rather than an estimate. Three placements that many readers would count as notable were coded as other exits: two positions at a North American research university and one funded doctoral and postdoctoral appointment in Europe. Coding them the other way would put the notable share above 60%. The conservative direction was chosen so that the finding survives an unfriendly reader recoding the marginal cases.
The judgement of capability is not mine. Each notable placement represents a decision by an independent third party — an admissions committee, a hiring manager, a research group with its own funding at stake — none of whom had any interest in this division’s internal politics. That is what the measurement is for: it substitutes external market judgement for the internal assessment that the organisation could not produce credibly.
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