Feedbacks
Monthly scores, quick feednotes, and ad-hoc 1:1 meetings for the talents engaged on this account.
My scoring patterns
A reflection surface - how you score across categories, where you're consistently strict or generous, and how the numbers drift month over month. Not visible to talents.
How you spread your scores
Every category score you've ever given, binned by half-point.
Solid centre of gravity around 4.0 - you treat 'Strong' as the default and reserve 5 for exceptional output. Healthy default.
Month over month
Your average composite by month.
Your scoring level has stayed consistent - neither getting harsher nor more lenient over the period shown.
By category
Where you tend to score higher and lower across all talents.
Each row averages every score you've given in that category. Tight spreads between rows usually mean the rubric is keeping you honest; wide spreads can be real (you really do see Growth lower) or a halo bias - same person, different lens.
Who you score where
Average composite per talent across all months.
- 1.Mariah Kleinmann4.61
- 2.Jenn Klein4.26
- 3.Anika Sharma4.09
- 4.Marco Kovacs3.98
- 5.Maya Khan3.58
- 6.Ilze Ozola3.10
Top-of-list talents may genuinely be your strongest - or they may benefit from a halo. If the same person sits at the top across every category, the rubric is letting you compress signal.
Calibration vs self-evaluations
Where the same month reads differently to you and the talent. Biggest gaps first.
- Maya KhanSelf < youAugust 2026You3.58Self3.00Δ -0.58 self below you
- Jenn KleinCalibratedSeptember 2026You4.43Self4.10Δ -0.33 self below you
- Jenn KleinCalibratedOctober 2026You4.25Self4.00Δ -0.25 self below you
- Mariah KleinmannCalibratedAugust 2026You4.43Self4.65Δ +0.22 self above you
- Jenn KleinCalibratedAugust 2026You4.10Self3.90Δ -0.20 self below you
Big positive gaps (self > you) often signal impostor or burnout risk that's worth a 1:1. Big negative gaps (self < you) suggest a confidence mismatch - they may be operating above their own perceived level.